1. The document discusses privacy preservation techniques for knowledge discovery from published data. It aims to anonymize data before release to limit disclosure risk while maximizing usefulness.
2. Three types of information disclosure are defined: membership, identity, and attribute disclosure. Identity disclosure occurs when a record is linked to an individual, while attribute disclosure reveals sensitive attributes about individuals.
3. Data anonymization techniques aim to prevent these disclosures. Explicit identifiers are first removed, then techniques like generalization and suppression are used to anonymize quasi-identifiers to prevent identity and attribute disclosures.
DATA PRIVACY IN AN AGE OF INCREASINGLY SPECIFIC AND PUBLICLY AVAILABLE DATA: ...Ted Myerson
DATA PRIVACY IN AN AGE OF INCREASINGLY SPECIFIC AND PUBLICLY AVAILABLE DATA: AN ANALYSIS OF RISK RESULTING FROM DATA TREATED USING ANONOS' JUST-IN-TIME-IDENTITY DYNAMIC DATA OBSCURITY METHODOLOGY
This document discusses ethical issues around social media and privacy in healthcare. It notes that Thailand saw a large increase in internet users between 2016 and 2017, with most users being aged 20-29. It then presents several case studies of situations where social media posts raised privacy or ethical concerns. The document outlines ethical principles like autonomy and non-maleficence that relate to health information privacy. It also reviews Thai laws around protecting patient health information privacy and provides resources on best practices for social media use by health professionals.
Privacy Preserving for Mobile Health DataIRJET Journal
This document discusses privacy issues related to sharing mobile health data and proposes using k-anonymity to address them. K-anonymity works by ensuring that the identifying attributes of each individual contained in released data cannot be distinguished from at least k-1 other individuals, making re-identification more difficult. Currently, health data can be re-identified by linking it to other publicly available data sources. The document outlines how existing approaches are insufficient and proposes applying k-anonymity to anonymize mobile health records before release in order to better protect patient privacy while allowing useful analysis of the data.
Modern medicine is facing a complex environment, not from medical technology but rather government regulations and information vulnerability. HIPPA is the government’s attempt to protect patient’s information yet this only addresses traditional record handling. The main threat is from the evolving security issues. Many medical offices and facilities have multiple areas of information security concerns. Physical security is often weak, office personnel are not always aware of security needs and application security and transmission protocols are not consistently maintained. Health insurance needs and general financial opportunity has created an emerging market in medical identity theft. Medical offices have the perfect storm of information collection, personal, credit, banking, health, and insurance. Thieves have realized that medical facilities have as much economic value as banks and the security is much easier to crack. Mostly committed by insiders, medical identity theft is a well-hidden information crime. In spite of its covert nature, the catastrophic ramification to the victims is overt. This information crime involves stealing patients’ records to impersonate the patients in an effort of obtaining health care services or claiming Medicare on the patients’ behalf. Unlike financial identity theft, there is a lack of recourse for the victims to recover from damages. Medical identity theft undermines the quality of health care information systems and enervates the information security of electronic patient record.
The document outlines four main categories of personal data by origin:
1. Provided data includes data disclosed directly by individuals through actions like loan applications or social media posts where they are aware of providing the data.
2. Observed data includes data recorded about individuals by others through means like online cookies, sensors, or CCTV cameras where individuals may or may not be aware of the data collection.
3. Derived data includes new data elements generated by analyzing other data through basic reasoning to detect patterns and create classifications, like calculating customer profitability.
4. Inferred data includes predictions and categorizations of individuals produced through probability-based analytics detecting correlations in data, like credit scores or disease risk predictions.
This document discusses the risks to patient privacy posed by electronic health records and health information exchanges given existing legislation and regulations. While laws like HIPAA provide some protections, they were created before widespread use of EHRs and do little to protect electronically stored data. Additionally, patients have little control over their health information under current policies. Attempts to balance privacy with the goals of improving population health through data sharing and EHR use have been challenging, with no clear resolution. Compromise is needed to define what information can be shared while maintaining patient anonymity.
Patient-Centric Privacy: Envisioning Collaboration Between Payers, Providers...Tyrone Grandison
Protection of personal healthcare information (PHI) has been as a significant hindrance to the acceptance, adoption and continued use of healthcare information technology (HIT). As nations and corporations encourage innovation in the healthcare sector for better outcomes for all its stakeholders, they are proceeding under a latent assumption – the equation of data stewardship with data ownership. This notion relegates the patient to the role of information provider and empowers infrastructure owners with data ownership rights. In this paper, we introduce Patient-Centric Privacy, which refers to 1) the recognition that patients are a fundamental and integral part of the disclosure, access and use processes, and 2) to the ability of the patient to control the release of their healthcare information.
DATA PRIVACY IN AN AGE OF INCREASINGLY SPECIFIC AND PUBLICLY AVAILABLE DATA: ...Ted Myerson
DATA PRIVACY IN AN AGE OF INCREASINGLY SPECIFIC AND PUBLICLY AVAILABLE DATA: AN ANALYSIS OF RISK RESULTING FROM DATA TREATED USING ANONOS' JUST-IN-TIME-IDENTITY DYNAMIC DATA OBSCURITY METHODOLOGY
This document discusses ethical issues around social media and privacy in healthcare. It notes that Thailand saw a large increase in internet users between 2016 and 2017, with most users being aged 20-29. It then presents several case studies of situations where social media posts raised privacy or ethical concerns. The document outlines ethical principles like autonomy and non-maleficence that relate to health information privacy. It also reviews Thai laws around protecting patient health information privacy and provides resources on best practices for social media use by health professionals.
Privacy Preserving for Mobile Health DataIRJET Journal
This document discusses privacy issues related to sharing mobile health data and proposes using k-anonymity to address them. K-anonymity works by ensuring that the identifying attributes of each individual contained in released data cannot be distinguished from at least k-1 other individuals, making re-identification more difficult. Currently, health data can be re-identified by linking it to other publicly available data sources. The document outlines how existing approaches are insufficient and proposes applying k-anonymity to anonymize mobile health records before release in order to better protect patient privacy while allowing useful analysis of the data.
Modern medicine is facing a complex environment, not from medical technology but rather government regulations and information vulnerability. HIPPA is the government’s attempt to protect patient’s information yet this only addresses traditional record handling. The main threat is from the evolving security issues. Many medical offices and facilities have multiple areas of information security concerns. Physical security is often weak, office personnel are not always aware of security needs and application security and transmission protocols are not consistently maintained. Health insurance needs and general financial opportunity has created an emerging market in medical identity theft. Medical offices have the perfect storm of information collection, personal, credit, banking, health, and insurance. Thieves have realized that medical facilities have as much economic value as banks and the security is much easier to crack. Mostly committed by insiders, medical identity theft is a well-hidden information crime. In spite of its covert nature, the catastrophic ramification to the victims is overt. This information crime involves stealing patients’ records to impersonate the patients in an effort of obtaining health care services or claiming Medicare on the patients’ behalf. Unlike financial identity theft, there is a lack of recourse for the victims to recover from damages. Medical identity theft undermines the quality of health care information systems and enervates the information security of electronic patient record.
The document outlines four main categories of personal data by origin:
1. Provided data includes data disclosed directly by individuals through actions like loan applications or social media posts where they are aware of providing the data.
2. Observed data includes data recorded about individuals by others through means like online cookies, sensors, or CCTV cameras where individuals may or may not be aware of the data collection.
3. Derived data includes new data elements generated by analyzing other data through basic reasoning to detect patterns and create classifications, like calculating customer profitability.
4. Inferred data includes predictions and categorizations of individuals produced through probability-based analytics detecting correlations in data, like credit scores or disease risk predictions.
This document discusses the risks to patient privacy posed by electronic health records and health information exchanges given existing legislation and regulations. While laws like HIPAA provide some protections, they were created before widespread use of EHRs and do little to protect electronically stored data. Additionally, patients have little control over their health information under current policies. Attempts to balance privacy with the goals of improving population health through data sharing and EHR use have been challenging, with no clear resolution. Compromise is needed to define what information can be shared while maintaining patient anonymity.
Patient-Centric Privacy: Envisioning Collaboration Between Payers, Providers...Tyrone Grandison
Protection of personal healthcare information (PHI) has been as a significant hindrance to the acceptance, adoption and continued use of healthcare information technology (HIT). As nations and corporations encourage innovation in the healthcare sector for better outcomes for all its stakeholders, they are proceeding under a latent assumption – the equation of data stewardship with data ownership. This notion relegates the patient to the role of information provider and empowers infrastructure owners with data ownership rights. In this paper, we introduce Patient-Centric Privacy, which refers to 1) the recognition that patients are a fundamental and integral part of the disclosure, access and use processes, and 2) to the ability of the patient to control the release of their healthcare information.
This document provides an overview of HIPAA training goals and requirements. It aims to inform staff about patient privacy and confidentiality rules under HIPAA. Key aspects covered include definitions of patient confidentiality and the Privacy and Security Rules. Consequences of HIPAA violations are also outlined, ranging from fines of $100 to $1.5 million per year depending on the violation. The document concludes with sample test questions to assess staff understanding of HIPAA guidelines.
Part of the "2016 Annual Conference: Big Data, Health Law, and Bioethics" held at Harvard Law School on May 6, 2016.
This conference aimed to: (1) identify the various ways in which law and ethics intersect with the use of big data in health care and health research, particularly in the United States; (2) understand the way U.S. law (and potentially other legal systems) currently promotes or stands as an obstacle to these potential uses; (3) determine what might be learned from the legal and ethical treatment of uses of big data in other sectors and countries; and (4) examine potential solutions (industry best practices, common law, legislative, executive, domestic and international) for better use of big data in health care and health research in the U.S.
The Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School 2016 annual conference was organized in collaboration with the Berkman Center for Internet & Society at Harvard University and the Health Ethics and Policy Lab, University of Zurich.
Learn more at http://petrieflom.law.harvard.edu/events/details/2016-annual-conference.
This document discusses privacy concerns regarding healthcare records and the importance of maintaining confidentiality. It outlines key aspects of the Health Insurance Portability and Accountability Act (HIPAA), including how electronic health records require new privacy protections. Examples are given of celebrities whose private medical information was inappropriately disclosed, violating privacy laws. Healthcare providers are responsible for safeguarding patient information and avoiding any misuse or disclosure of private health details.
1) The document discusses privacy and security risks associated with digital health data. It provides statistics showing that attacks on healthcare organizations' data have increased by 1.25 times in the last five years compared to previously.
2) On average, each data breach incident in healthcare organizations results in the compromise of over 18,000 patient records. The cost of each breached record is also highest for healthcare at $363.
3) Criminal attacks are now the leading cause of data breaches in healthcare, surpassing unintentional leaks and insider breaches. The document recommends measures to reduce privacy and security risks when integrating digital health data.
Part of the "2016 Annual Conference: Big Data, Health Law, and Bioethics" held at Harvard Law School on May 6, 2016.
This conference aimed to: (1) identify the various ways in which law and ethics intersect with the use of big data in health care and health research, particularly in the United States; (2) understand the way U.S. law (and potentially other legal systems) currently promotes or stands as an obstacle to these potential uses; (3) determine what might be learned from the legal and ethical treatment of uses of big data in other sectors and countries; and (4) examine potential solutions (industry best practices, common law, legislative, executive, domestic and international) for better use of big data in health care and health research in the U.S.
The Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School 2016 annual conference was organized in collaboration with the Berkman Center for Internet & Society at Harvard University and the Health Ethics and Policy Lab, University of Zurich.
Learn more at http://petrieflom.law.harvard.edu/events/details/2016-annual-conference.
Open Government Data & Privacy ProtectionSylvia Ogweng
The document discusses privacy issues related to open government data initiatives. It notes that while open data brings benefits, privacy concerns have slowed its adoption. The types of government data - infrastructure, public services, and personal - present different privacy risks. Maintaining privacy involves de-identifying and anonymizing data, but these processes do not always guarantee privacy. North American governments are working to address privacy through funding for privacy-enhancing technologies and focusing on privacy within specific domains like healthcare and as an extension of security.
Did you know that on average, it takes 5 days to provision a new employee or contingent worker? Are you traversing complex digital environments, growing regulatory requirements, and exploding personnel growth?
What if you could onboard employees, have them provisioned, access approved, and onboard in half the time? With SailPoint’s automated identity solutions, you can.
The document discusses challenges small healthcare providers face in complying with HIPAA security regulations. It notes that while HIPAA and HITECH were meant to improve privacy and security of electronic health records, smaller practices and hospitals struggle with understanding and implementing security standards due to limited resources and technical expertise. This leaves them at greater risk of data breaches compared to larger organizations. Revising HIPAA and providing better guidance tailored to small providers' needs could help address these challenges.
Ehr by jessica austin, shaun baker, victoria blankenship and kayla borokayla_ann_30
This document provides an overview of electronic health records (EHR) including what they are, key components, considerations for implementation, and security and costs. It discusses that EHRs provide a centralized digital patient record accessible by healthcare providers. The eight essential components that must be included are things like health information, order entry, decision support, and administrative functions. Proper implementation requires input from various stakeholders like medical staff, IT, and leadership. Security and privacy are also important considerations, as are the financial costs of purchasing and maintaining an EHR system.
This document discusses strategies for implementing eBusiness initiatives in the healthcare industry. It notes that while modern medicine utilizes advanced technologies, healthcare organizations still rely heavily on paper-based systems. eBusiness can bridge this gap by bringing parties together electronically to complete transactions. However, healthcare organizations must address special considerations like HIPAA compliance and protecting sensitive patient information when moving to eBusiness models. Proper planning of user roles, infrastructure, security features, and agreements with business partners is needed.
Cost of Data Breah in Healthcare_Quinlan, Courtneycourtneyquinlan
This document discusses the high costs of data breaches in the healthcare industry. It notes that the rise of electronic health records has led to more data theft opportunities. Data breaches can result in identity theft and fraudulent charges against patients. They also cost organizations millions to respond to. The document examines the financial costs of data breaches to both individuals and organizations. It then discusses strategies organizations can take to prevent data breaches, such as encrypting data, training employees, and developing a formal security plan.
IRJET - A Survey on Blockchain Technology for Electronic Health RecordIRJET Journal
This document discusses using blockchain technology to improve electronic health records (EHRs). It begins with an abstract that outlines the goals of using blockchain for EHRs, including improving healthcare efficiency and access while reducing costs. The document then provides background on EHRs and issues like data security, ownership and integrity. It proposes using blockchain to securely store and share medical records in a decentralized network. The document outlines several proposed blockchain applications for EHRs, including distributed storage of health data collected from IoT devices, and allowing patients more control over access to their records. It discusses challenges like scalability and interoperability, and concludes that blockchain technology has potential to help address current problems with EHR systems.
This document discusses national trends in health information exchange and granular consent. It begins with introductions and an agenda, then covers learning objectives about types of HIE, sensitive health data, and granular consent requirements. It demonstrates HIE through a game and discusses query-based, directed, and consumer-mediated exchange. It also covers fair information practice principles, privacy by design principles, examples of sensitive data, and current provider processes for release of information. The document discusses the current state of HIE, core consent options, how states are handling consent, relevant laws, and a comparison of HIE consent capabilities. It closes by asking what the future of HIE and consent should look like.
Protection and immunity under Cybersecurity Information Sharing ActDavid Sweigert
This document provides guidance for non-federal entities on sharing cyber threat indicators and defensive measures with federal entities under the Cybersecurity Information Sharing Act of 2015. It defines key terms like cyber threat indicator, defensive measure, and information protected by privacy laws. It also explains how non-federal entities can share indicators and measures through the Department of Homeland Security's Automated Indicator Sharing system or other means. The document aims to help non-federal entities properly share information for cybersecurity purposes while protecting privacy.
Welcome to the first Verizon Protected Health Information Data Breach Report (PHIDBR).
We’re the same team that has brought you the Verizon Data Breach Investigations Report
(DBIR) since 2008, and we are excited to revisit some of that data and bring in
some new incidents for this report.
The purpose of this study is to shed light on the problem of medical data loss—how it is
disclosed, who is causing it and what can be done to combat it. This is a far-reaching
problem that impacts not only organizations that are victims of these breaches, but also
doctor-patient relationships. And it can have consequences that spread more broadly
than just those directly affected by the incidents.
1) Caroline Rivett discusses how cloud technology can support digital health services but also risks to sensitive medical information stored in the cloud.
2) Medical information is rapidly expanding due to devices that transmit health data, personal health apps, genetic sequencing projects, and growth of electronic health records.
3) Key considerations for using cloud technology include ensuring security of medical data from hackers or nation states, as well as complying with privacy laws and regulations regarding sensitive personal health information.
The document discusses the utilization of foundry waste sand in the preparation of concrete. It presents the results of experiments conducted to study the compressive strength, split tensile strength, and flexural strength of M20 and M25 grade concrete containing 0%, 10%, and 100% replacement of foundry waste sand in place of fine aggregate. The tests were conducted at curing periods of 7, 28, and 56 days. The results showed that 100% replacement of foundry waste sand can be used for M20 and M25 grade concrete based on the compressive strengths achieved at different curing periods being comparable to control mixes. Flexural and split tensile strengths were also found to be comparable between control mixes and mixes with foundry
A Challenge to Analyze and Detect Altered Human FingerprintsIOSR Journals
This document analyzes and proposes a method to detect altered human fingerprints. It discusses three main types of fingerprint alterations: obliteration, distortion, and imitation. It also outlines a system to 1) analyze altered fingerprints, 2) classify alterations, 3) demonstrate detection using a fingerprint database, and 4) develop an automatic detection technique using image processing and matching algorithms. The goal is to highlight the problem of altered fingerprints and propose an algorithm to identify them.
This document proposes a Quorum-based Medium Access Control (QMAC) protocol to improve energy efficiency in wireless sensor networks. QMAC enables sensor nodes to sleep longer under light traffic loads by only waking up during scheduled "quorum times". Each node selects one row and column from a grid as its quorum set. This ensures any two nodes' quorums will intersect at some time, allowing communication while keeping individual duty cycles low. Results show QMAC conserves more energy and maintains low latency compared to existing protocols that require waking at every time frame regardless of traffic. QMAC selectively wakes sensor nodes only when needed to balance energy savings and communication ability.
This document describes a proposed cloudburst prediction system using an Arduino board connected to a rain gauge to calculate rainfall intensity in real-time. A float switch and submersible pump in the rain gauge monitor water levels. The Arduino records rainfall data and triggers alarms at different intensity thresholds, with the highest threshold signaling evacuation. Nearby residents would receive alert messages via a module that broadcasts to cell phones. The system aims to provide low-cost, real-time cloudburst prediction compared to existing weather forecasting and satellite-based methods.
This document proposes a new encryption scheme called compact summation key encryption for secure data sharing in hybrid cloud storage. It aims to address limitations of existing approaches like predefined hierarchical schemes, attribute-based encryption, and identity-based encryption which cannot provide security to individual files or have non-constant size keys. The new scheme uses five algorithms: setup, key generation, encryption, extraction and decryption. It generates constant size public and master secret keys. Encryption uses file indexes and bilinear groups to create ciphertexts. Extraction combines decryption keys into a single compact summation key using bilinear pairing operations. This key can then decrypt ciphertexts for multiple file indexes, improving flexibility and efficiency of secure data sharing in cloud storage.
This document provides an overview of HIPAA training goals and requirements. It aims to inform staff about patient privacy and confidentiality rules under HIPAA. Key aspects covered include definitions of patient confidentiality and the Privacy and Security Rules. Consequences of HIPAA violations are also outlined, ranging from fines of $100 to $1.5 million per year depending on the violation. The document concludes with sample test questions to assess staff understanding of HIPAA guidelines.
Part of the "2016 Annual Conference: Big Data, Health Law, and Bioethics" held at Harvard Law School on May 6, 2016.
This conference aimed to: (1) identify the various ways in which law and ethics intersect with the use of big data in health care and health research, particularly in the United States; (2) understand the way U.S. law (and potentially other legal systems) currently promotes or stands as an obstacle to these potential uses; (3) determine what might be learned from the legal and ethical treatment of uses of big data in other sectors and countries; and (4) examine potential solutions (industry best practices, common law, legislative, executive, domestic and international) for better use of big data in health care and health research in the U.S.
The Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School 2016 annual conference was organized in collaboration with the Berkman Center for Internet & Society at Harvard University and the Health Ethics and Policy Lab, University of Zurich.
Learn more at http://petrieflom.law.harvard.edu/events/details/2016-annual-conference.
This document discusses privacy concerns regarding healthcare records and the importance of maintaining confidentiality. It outlines key aspects of the Health Insurance Portability and Accountability Act (HIPAA), including how electronic health records require new privacy protections. Examples are given of celebrities whose private medical information was inappropriately disclosed, violating privacy laws. Healthcare providers are responsible for safeguarding patient information and avoiding any misuse or disclosure of private health details.
1) The document discusses privacy and security risks associated with digital health data. It provides statistics showing that attacks on healthcare organizations' data have increased by 1.25 times in the last five years compared to previously.
2) On average, each data breach incident in healthcare organizations results in the compromise of over 18,000 patient records. The cost of each breached record is also highest for healthcare at $363.
3) Criminal attacks are now the leading cause of data breaches in healthcare, surpassing unintentional leaks and insider breaches. The document recommends measures to reduce privacy and security risks when integrating digital health data.
Part of the "2016 Annual Conference: Big Data, Health Law, and Bioethics" held at Harvard Law School on May 6, 2016.
This conference aimed to: (1) identify the various ways in which law and ethics intersect with the use of big data in health care and health research, particularly in the United States; (2) understand the way U.S. law (and potentially other legal systems) currently promotes or stands as an obstacle to these potential uses; (3) determine what might be learned from the legal and ethical treatment of uses of big data in other sectors and countries; and (4) examine potential solutions (industry best practices, common law, legislative, executive, domestic and international) for better use of big data in health care and health research in the U.S.
The Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School 2016 annual conference was organized in collaboration with the Berkman Center for Internet & Society at Harvard University and the Health Ethics and Policy Lab, University of Zurich.
Learn more at http://petrieflom.law.harvard.edu/events/details/2016-annual-conference.
Open Government Data & Privacy ProtectionSylvia Ogweng
The document discusses privacy issues related to open government data initiatives. It notes that while open data brings benefits, privacy concerns have slowed its adoption. The types of government data - infrastructure, public services, and personal - present different privacy risks. Maintaining privacy involves de-identifying and anonymizing data, but these processes do not always guarantee privacy. North American governments are working to address privacy through funding for privacy-enhancing technologies and focusing on privacy within specific domains like healthcare and as an extension of security.
Did you know that on average, it takes 5 days to provision a new employee or contingent worker? Are you traversing complex digital environments, growing regulatory requirements, and exploding personnel growth?
What if you could onboard employees, have them provisioned, access approved, and onboard in half the time? With SailPoint’s automated identity solutions, you can.
The document discusses challenges small healthcare providers face in complying with HIPAA security regulations. It notes that while HIPAA and HITECH were meant to improve privacy and security of electronic health records, smaller practices and hospitals struggle with understanding and implementing security standards due to limited resources and technical expertise. This leaves them at greater risk of data breaches compared to larger organizations. Revising HIPAA and providing better guidance tailored to small providers' needs could help address these challenges.
Ehr by jessica austin, shaun baker, victoria blankenship and kayla borokayla_ann_30
This document provides an overview of electronic health records (EHR) including what they are, key components, considerations for implementation, and security and costs. It discusses that EHRs provide a centralized digital patient record accessible by healthcare providers. The eight essential components that must be included are things like health information, order entry, decision support, and administrative functions. Proper implementation requires input from various stakeholders like medical staff, IT, and leadership. Security and privacy are also important considerations, as are the financial costs of purchasing and maintaining an EHR system.
This document discusses strategies for implementing eBusiness initiatives in the healthcare industry. It notes that while modern medicine utilizes advanced technologies, healthcare organizations still rely heavily on paper-based systems. eBusiness can bridge this gap by bringing parties together electronically to complete transactions. However, healthcare organizations must address special considerations like HIPAA compliance and protecting sensitive patient information when moving to eBusiness models. Proper planning of user roles, infrastructure, security features, and agreements with business partners is needed.
Cost of Data Breah in Healthcare_Quinlan, Courtneycourtneyquinlan
This document discusses the high costs of data breaches in the healthcare industry. It notes that the rise of electronic health records has led to more data theft opportunities. Data breaches can result in identity theft and fraudulent charges against patients. They also cost organizations millions to respond to. The document examines the financial costs of data breaches to both individuals and organizations. It then discusses strategies organizations can take to prevent data breaches, such as encrypting data, training employees, and developing a formal security plan.
IRJET - A Survey on Blockchain Technology for Electronic Health RecordIRJET Journal
This document discusses using blockchain technology to improve electronic health records (EHRs). It begins with an abstract that outlines the goals of using blockchain for EHRs, including improving healthcare efficiency and access while reducing costs. The document then provides background on EHRs and issues like data security, ownership and integrity. It proposes using blockchain to securely store and share medical records in a decentralized network. The document outlines several proposed blockchain applications for EHRs, including distributed storage of health data collected from IoT devices, and allowing patients more control over access to their records. It discusses challenges like scalability and interoperability, and concludes that blockchain technology has potential to help address current problems with EHR systems.
This document discusses national trends in health information exchange and granular consent. It begins with introductions and an agenda, then covers learning objectives about types of HIE, sensitive health data, and granular consent requirements. It demonstrates HIE through a game and discusses query-based, directed, and consumer-mediated exchange. It also covers fair information practice principles, privacy by design principles, examples of sensitive data, and current provider processes for release of information. The document discusses the current state of HIE, core consent options, how states are handling consent, relevant laws, and a comparison of HIE consent capabilities. It closes by asking what the future of HIE and consent should look like.
Protection and immunity under Cybersecurity Information Sharing ActDavid Sweigert
This document provides guidance for non-federal entities on sharing cyber threat indicators and defensive measures with federal entities under the Cybersecurity Information Sharing Act of 2015. It defines key terms like cyber threat indicator, defensive measure, and information protected by privacy laws. It also explains how non-federal entities can share indicators and measures through the Department of Homeland Security's Automated Indicator Sharing system or other means. The document aims to help non-federal entities properly share information for cybersecurity purposes while protecting privacy.
Welcome to the first Verizon Protected Health Information Data Breach Report (PHIDBR).
We’re the same team that has brought you the Verizon Data Breach Investigations Report
(DBIR) since 2008, and we are excited to revisit some of that data and bring in
some new incidents for this report.
The purpose of this study is to shed light on the problem of medical data loss—how it is
disclosed, who is causing it and what can be done to combat it. This is a far-reaching
problem that impacts not only organizations that are victims of these breaches, but also
doctor-patient relationships. And it can have consequences that spread more broadly
than just those directly affected by the incidents.
1) Caroline Rivett discusses how cloud technology can support digital health services but also risks to sensitive medical information stored in the cloud.
2) Medical information is rapidly expanding due to devices that transmit health data, personal health apps, genetic sequencing projects, and growth of electronic health records.
3) Key considerations for using cloud technology include ensuring security of medical data from hackers or nation states, as well as complying with privacy laws and regulations regarding sensitive personal health information.
The document discusses the utilization of foundry waste sand in the preparation of concrete. It presents the results of experiments conducted to study the compressive strength, split tensile strength, and flexural strength of M20 and M25 grade concrete containing 0%, 10%, and 100% replacement of foundry waste sand in place of fine aggregate. The tests were conducted at curing periods of 7, 28, and 56 days. The results showed that 100% replacement of foundry waste sand can be used for M20 and M25 grade concrete based on the compressive strengths achieved at different curing periods being comparable to control mixes. Flexural and split tensile strengths were also found to be comparable between control mixes and mixes with foundry
A Challenge to Analyze and Detect Altered Human FingerprintsIOSR Journals
This document analyzes and proposes a method to detect altered human fingerprints. It discusses three main types of fingerprint alterations: obliteration, distortion, and imitation. It also outlines a system to 1) analyze altered fingerprints, 2) classify alterations, 3) demonstrate detection using a fingerprint database, and 4) develop an automatic detection technique using image processing and matching algorithms. The goal is to highlight the problem of altered fingerprints and propose an algorithm to identify them.
This document proposes a Quorum-based Medium Access Control (QMAC) protocol to improve energy efficiency in wireless sensor networks. QMAC enables sensor nodes to sleep longer under light traffic loads by only waking up during scheduled "quorum times". Each node selects one row and column from a grid as its quorum set. This ensures any two nodes' quorums will intersect at some time, allowing communication while keeping individual duty cycles low. Results show QMAC conserves more energy and maintains low latency compared to existing protocols that require waking at every time frame regardless of traffic. QMAC selectively wakes sensor nodes only when needed to balance energy savings and communication ability.
This document describes a proposed cloudburst prediction system using an Arduino board connected to a rain gauge to calculate rainfall intensity in real-time. A float switch and submersible pump in the rain gauge monitor water levels. The Arduino records rainfall data and triggers alarms at different intensity thresholds, with the highest threshold signaling evacuation. Nearby residents would receive alert messages via a module that broadcasts to cell phones. The system aims to provide low-cost, real-time cloudburst prediction compared to existing weather forecasting and satellite-based methods.
This document proposes a new encryption scheme called compact summation key encryption for secure data sharing in hybrid cloud storage. It aims to address limitations of existing approaches like predefined hierarchical schemes, attribute-based encryption, and identity-based encryption which cannot provide security to individual files or have non-constant size keys. The new scheme uses five algorithms: setup, key generation, encryption, extraction and decryption. It generates constant size public and master secret keys. Encryption uses file indexes and bilinear groups to create ciphertexts. Extraction combines decryption keys into a single compact summation key using bilinear pairing operations. This key can then decrypt ciphertexts for multiple file indexes, improving flexibility and efficiency of secure data sharing in cloud storage.
This document describes a system for extracting named entities and their relationships from unstructured text data using n-gram features. It uses a hidden Markov model to extract and classify entities into types like person, location, organization. It then uses a conditional random field with kernel approach to detect relationships between the extracted entities. The system takes unstructured text as input, performs preprocessing like tokenization and stop word removal, extracts n-gram, part-of-speech and lexicon features which are then combined and used to train the HMM model to classify entities and CRF model to detect relationships between entities.
This document describes an improved Max-Min scheduling algorithm that considers additional constraints beyond just completion time. The improved algorithm calculates a proportional fairness score for each job/task based on its size, completion time, payload storage rate, and RAM requirements. It then sorts the jobs based on these scores to prioritize jobs with the highest scores, addressing limitations of the traditional Max-Min algorithm that only considers completion time. The algorithm is evaluated using a simulator with scientific workflows and workloads. Results show the improved algorithm efficiently schedules jobs while accounting for multiple constraints.
Longitudinal Skeleton Dimensionality Characteristics of Nigerian Junior Male ...IOSR Journals
This document discusses a study that examined the longitudinal skeleton dimensionality (LSD) characteristics of 106 Nigerian junior male handball players in relation to their playing positions. The study measured body height, arm span, arm length, hand length, and leg length of players in goalkeeper, inside back, centre back, pivot, and wing positions. It found that inside back players were significantly taller with longer arm spans than other positions. Goalkeepers had the longest arm lengths on average, while centre backs had the lowest averages across measurements. The results suggest superior height and arm span are important for inside backs, and could help identify talent for player development and selection based on position.
This document proposes a novel method for generating secret keys for stream ciphers used in secure communication. The method constructs keys from digital images by:
1. Separating the image into grayscale color channels.
2. Calculating the number of pixels in intensity ranges for each channel.
3. Comparing the pixel counts to a threshold to generate a 50-bit key for each channel.
The keys are then used to encrypt messages by applying XOR operations between the plaintext and key bits. The same method decrypts ciphertexts by reversing the XOR operations. Examples demonstrate encrypting messages with keys generated from images.
The document discusses using Learning Factor Analysis (LFA), an educational data mining technique, to model student knowledge based on student-tutor interaction log data. LFA uses a multiple logistic regression model with difficulty factors defined by subject experts to quantify skills. A combinatorial search method called A* search is used to select the best-fitting model. The document illustrates applying LFA to data from an online math tutor, identifying 5 skills and presenting the results of the logistic regression modeling, including fit statistics and learning rates for skills. Learning curves are used to visualize student performance over time.
This document proposes an autonomous self-assessment application that can intelligently determine the running time of processes based on the processor state and process priority. It uses several scheduling algorithms like shortest job first, first come first serve, priority, round robin, and multilevel queue scheduling. The application divides work into predicting process running times and scheduling a series of processes to optimize results. It calculates process weights, stores running time data, and uses that historical data to predict future running times. It then schedules processes using a priority-based approach and adjusts priorities if smaller processes are waiting too long. The results show the application can determine expected running times for given processes using this approach.
The document describes a laser pointer interaction system for manipulating 3D medical images on a large display in operating rooms. The system uses a webcam to detect the laser spot and track its movements. It recognizes two types of gestures - a circle gesture with two directions and a line gesture with four directions. The system was tested on 15 subjects performing rotation and zooming tasks on a 3D model. Experiments showed that the dynamic time warping algorithm recognized gestures with 89.6% accuracy and was faster than the 1$ recognizer algorithm. The laser pointer system provides a potential hands-free alternative to traditional mouse/keyboard control in operating rooms.
An Enhanced ILD Diagnosis Method using DWTIOSR Journals
1. The document describes an enhanced method for diagnosing Interstitial Lung Disease (ILD) using Discrete Wavelet Transform (DWT).
2. The method involves acquiring CT lung images, enhancing edges using DWT, segmenting the lung region, segmenting blood vessels, extracting texture features from vessels, and classifying images as normal or ILD using Fuzzy Support Vector Machines (FSVM).
3. Wavelet edge enhancement improves segmentation of vessels. Feature extraction using co-occurrence matrices and discriminant analysis reduces dimensions before FSVM classification. The method achieves accurate ILD diagnosis compared to existing approaches.
This document discusses the role of project management consultancy (PMC) in infrastructure projects. It outlines the various roles of a PMC at different stages of a road construction project, including pre-tendering, tendering, and post-tendering stages. In the pre-tendering stage, the PMC is involved in activities like conceptual planning, cost estimation, feasibility studies, surveys, and design. In the tendering stage, the PMC assists with drafting tender documents, managing the bidding process, and contract award. In the post-tendering stage, the PMC's roles include supervision, quality control, progress monitoring, documentation, and handling issues during project execution and operation. Overall, the PMC aims to
This document discusses bio-CNG (compressed biogas) as a transportation fuel alternative to fossil fuels. It begins by introducing the problems of increasing fossil fuel usage and outlines biogas production methods from waste sources. The main processes for cleaning biogas - including water scrubbing, pressure swing adsorption, amine adsorption and membrane permeation - are then summarized. The paper also covers converting cleaned biogas into bio-CNG and its storage. Key advantages of bio-CNG are highlighted such as reduced greenhouse gas emissions compared to diesel and potential cost savings versus petrol and diesel. The conclusion promotes bio-CNG as a viable replacement for fossil fuels that could support more sustainable living.
This document summarizes a study on traffic noise pollution in the city of Anand, India. The study found that the main sources of noise pollution are loudhailers and vehicles. However, religious noises affect the female population more than the male population. Effects of noise pollution include psychological impacts like depression and sleep disturbance, as well as physiological impacts like hearing loss. Most respondents complain to authorities to stop noise, though few contact the police. Public education campaigns are seen as the best approach to control noise pollution by respondents. Government and non-governmental organizations can play an important role in addressing this issue.
Bipolar Disorder Investigation Using Modified Logistic Ridge EstimatorIOSR Journals
This study investigates factors contributing to bipolar disorder among 109 teaching and non-teaching university staff using a modified logistic ridge estimator. Sex, age, occupation, and body mass index were analyzed as factors beyond traditional genetic, environmental, and neurochemical factors. The modified logistic ridge estimator was found to have smaller standard errors than the standard logistic ridge estimator or logistic estimator. The results found that sex, age, and body mass index significantly contribute to bipolar disorder, with men and older adults (≥40 years) being more predisposed, and higher body mass index positively correlated with bipolar disorder. The probability of bipolar tendency was highest for non-teaching males aged 40 or older with the highest recorded body mass index
This document discusses a study that investigated the bending strength of reactive powder concrete (RPC) using Ahlat stone as aggregate. Ahlat stone is a natural stone found in the Ahlat region of Turkey that has traditionally been used in local architecture. The study aimed to expand the use of Ahlat stone in concrete construction. Tests were conducted to determine the bending strength of natural Ahlat stone samples and RPC samples with and without fibers after 7 and 28 days of curing. The results showed that natural Ahlat stone had lower bending strength than fiberless and fibrous RPC containing Ahlat stone aggregate.
This document summarizes research on elevated temperature wire drawing using Azadirachta indica and Jatropha curcas seed oils as lubricants. Experiments were conducted drawing mild steel and medium carbon steel wire at temperatures ranging from ambient to 850°C. The maximum reduction in cross-sectional area achieved was 40-48%. Both lubricants proved effective across all temperatures. Tungsten carbide dies performed best. Drawing forces decreased with increasing temperature due to lower flow stresses. However, friction also increased, balancing the effects. The lubricants effectively addressed tribology issues at higher temperatures. In conclusion, wire can be successfully drawn at elevated temperatures using these natural oil lubricants.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Page 9 of 15Capstone ProjectYaima OrtizIDS-4934.docxkarlhennesey
Page 9 of 15
Capstone Project
Yaima Ortiz
IDS-4934
March 1st, 2020
Abstract
Topic:
Privacy- What medical information should be confidential? Who, if anybody, should have access to medical records?
Thesis Statement
In healthcare centers and overall privacy is the right of every US citizen that should be protected in all its forms by the healthcare organization.
Rationale
1. The purpose of this paper is to identify why security measures are necessary to protect one’s privacy in the medical industry.
2. There are numerous laws, policies and healthcare organizational rules and regulations and statistics that would be helpful for conducting this research.
3. Privacy of a person whether this is me or you, is important then everything. I want to talk on this topic because I think most of us do not know what is happening to us.
4. I have selected textual analysis of books and available internet sources. The reason of this limited research methodology is that I cannot perform field study because of shortage of time.
Rough Draft Ideas
Identity theft in healthcare industry become a common practice and leads to information leakage that may destroy someone’s life. We can eliminate this human right violation by enforcing effective and practical laws. Healthcare organizations should understand their responsibilities and tighten security to protect information of patients.
Table of Contents
Introduction 3
Overview of Privacy Protections with Respect to Medical Records 4
Data Breaches in the Healthcare Industry 5
Healthcare is the biggest Target for Cyber Attack 7
Penalties and Punishments for Hacking Personal Information 9
Penalties 9
Devastating Consequences of Healthcare Data Breaches 10
Conclusion 10
Recommendations 11
Bibliography 12
Introduction
While operating in healthcare organizations need to gather patient’s information that is mostly personal information. It is the moral and legal responsibility of health care organizations to protect the information of their patients and do not share it with people outside of the organization without the patient’s consent. Protecting patient’s information is a crucial element of respect and essential for patients' autonomy and trust in the organization — the US healthcare industry currently facing patient mistrust that is caused because of a lack of trust. When patients experience a lack of confidence they do not share their information with a healthcare professional that causes ineffective treatment. In a 2018 study, Levy, Scherer, Zikmund-Fisher, Larkin, Barnes, & Fagerlin concluded that approximately 81.1% of people withheld medically relevant information from their health-care providers. Patients fail to disclose medically relevant information in front of their clinicians undermine their health and cause patient harm (Levy, 2018).
There are numerous components of patient privacy in healthcare that are personal space, religious and cultural affiliations, physical privacy ...
Page 9 of 15Capstone ProjectYaima OrtizIDS-4934.docxhoney690131
Page 9 of 15
Capstone Project
Yaima Ortiz
IDS-4934
March 1st, 2020
Abstract
Topic:
Privacy- What medical information should be confidential? Who, if anybody, should have access to medical records?
Thesis Statement
In healthcare centers and overall privacy is the right of every US citizen that should be protected in all its forms by the healthcare organization.
Rationale
1. The purpose of this paper is to identify why security measures are necessary to protect one’s privacy in the medical industry.
2. There are numerous laws, policies and healthcare organizational rules and regulations and statistics that would be helpful for conducting this research.
3. Privacy of a person whether this is me or you, is important then everything. I want to talk on this topic because I think most of us do not know what is happening to us.
4. I have selected textual analysis of books and available internet sources. The reason of this limited research methodology is that I cannot perform field study because of shortage of time.
Rough Draft Ideas
Identity theft in healthcare industry become a common practice and leads to information leakage that may destroy someone’s life. We can eliminate this human right violation by enforcing effective and practical laws. Healthcare organizations should understand their responsibilities and tighten security to protect information of patients.
Table of Contents
Introduction 3
Overview of Privacy Protections with Respect to Medical Records 4
Data Breaches in the Healthcare Industry 5
Healthcare is the biggest Target for Cyber Attack 7
Penalties and Punishments for Hacking Personal Information 9
Penalties 9
Devastating Consequences of Healthcare Data Breaches 10
Conclusion 10
Recommendations 11
Bibliography 12
Introduction
While operating in healthcare organizations need to gather patient’s information that is mostly personal information. It is the moral and legal responsibility of health care organizations to protect the information of their patients and do not share it with people outside of the organization without the patient’s consent. Protecting patient’s information is a crucial element of respect and essential for patients' autonomy and trust in the organization — the US healthcare industry currently facing patient mistrust that is caused because of a lack of trust. When patients experience a lack of confidence they do not share their information with a healthcare professional that causes ineffective treatment. In a 2018 study, Levy, Scherer, Zikmund-Fisher, Larkin, Barnes, & Fagerlin concluded that approximately 81.1% of people withheld medically relevant information from their health-care providers. Patients fail to disclose medically relevant information in front of their clinicians undermine their health and cause patient harm (Levy, 2018).
There are numerous components of patient privacy in healthcare that are personal space, religious and cultural affiliations, physical privacy.
Running head DATA PRIVACY 1 DATA PRIVACY10Short- and .docxtodd271
Running head: DATA PRIVACY 1
DATA PRIVACY 10
Short- and Long-Term Effects of Sharing Personal and Medical Data
1. Introduction
The advancement in technology has led to the production of a large volume of human information for economic and medicinal uses. Information plays a significant part in the identification of social issues and helps effective device solutions to daily problems in human life. Although personal and medical info is essential, there are at times when people who have access to the data misuse it for financial gains. When it comes to data privacy, a lot of controversies exists as most people are unaware of their right to privacy. People use internet services where they input sensitive personal or medical data. Most online platforms such as social media have become a significant source of traffic for advertising consumer products and services.
Most websites use the information which the users enter and optimize the searches on the internet. Search engines such as Google use browser cookies to direct specific ads to some clients depending on the critical works in the history of individual searches. Online targeted adverts seem like exploitation of internet users or a form of manipulating people to buy some products from an online store. The psychology of the ads employs smart algorithms which use human information to identify what someone likes on the internet. It is a form of attracting potential clients or generating substantial traffic to a website. As the online adverts become a social issue, there is a need for awareness concerning the impacts of sharing personal or medical info over the internet. Some people claim that information leakage leads to misuse of human information and hence there is a need to educate individuals on temporary and lasting consequences of sharing personal info.
2. Personal Data
In Europe, personal info means more than human names alone. A simple detail is shown in the identification documents, bills, and other critical documents which reveal the identity of an individual. Information is a broad field of knowledge which helps Europeans to be aware of their cultural identity, internet addresses, and even critical locational data. In Europe and other modern countries with advanced internet infrastructure, an IP address is personal information which is essential for human identification (ICO, 2018). Own names, addresses, and location data identifies are used to keep a human updated with the time of his location since it is provided by the satellites, and hence information plays an essential role in the process of identifying what people who use the internet. Internet service users do love various things which they search on the internet in their life and the intelligence gathered is then applied in designing catchy adverts which generate traffic to people and benefit some individuals.
There is various information which people input in website forms to open accounts on th.
PLEASE POST EACH DISCUSSION SEPARATELYEach healthcare organisamirapdcosden
PLEASE POST EACH DISCUSSION SEPARATELY
Each healthcare organization has its own internal policies related to how data is managed. There are also
federal guidelines and regulations
regarding the use of patient data. The data harvested by healthcare organizations is no longer uniquely derived from HIT systems.
Wearable technologies
have emerged in the market. Mega companies like Apple and Samsung, have also teamed up with some telehealth platforms to connect doctors, institutions, and insurance companies.
Evaluate the impact of data derived from wearable technology on healthcare technology.
Include the following aspects in the discussion:
Select
Apple's Health Kit
or another consumer platform of your choice.
Discuss how the consumer wearable market is changing the healthcare delivery process.
Summarize why cybersecurity continues to be a major obstacle to consumer wearable adoption specifically in the H.I.T. space.
Discuss your personal perspective on how the lack of ethnic diversity in data collection impacts the future of healthcare research.
REPLY TO 2 OF MY CLASSMATES DISCUSSION TO THE ABOVE QUESTIONS AND EXPLAIN WHY YOU AGREE. MINIMUM OF 150 WORDS EACH
CLASSMATE POST 1
The Apple Health Kit and the many other wearable device technology is creating data in a bountiful way. What the Health Kit does is collect the relevant data and process it specifically for the person wearing the device. The device monitors things such as blood pressure, heart rate, calories burned in a day etc. and that data can be directly sent to your doctor as well. The wearable market is impacting healthcare in that it is making it more accessible, and your data is becoming more personable. If something is on you every day it will learn your habits, your sleep patterns, your calories burned each day and be able to tell you where improvements could be made and commend a healthy change. According to the International Journal of Recent Research Aspects the number of connected medical devices is expected to increase from 10 billion to 50 billion over the next decade (Chawala, 2020). With an increased number of connected devices, it also increases the likelihood of someone accessing private information that is not a health care team member. Cyber security is becoming as important as homeland security as most attackers can do the same damage anonymously and behind a computer screen. The problem with wearable devices is that they are connected mainly via Bluetooth which is a public network were others could see the device connected. Secondly, the data that is being sent or monitored could be interfered while in transmission or an apple watch or device could be stolen that has all the owner’s information freely on it. Despite the tracking and privacy networks they have installed, it is easily overcome or stolen off a wrist.
The ethical concerns in the lack of diversity in data entry is inter ...
Seventeen pages of detailed comments are attached.
To briefly summarize. We argue that workplace injury and illness records should be made more widely available because releasing these data has substantial potential individual, research, policy, and economic benefits. However, OSHA has a responsibility to apply best practices to manage data privacy and mitigate potential harms to individuals that might arise from data release.
The complexity, detail, richness, and emerging uses for data create significant uncertainties about the ability of traditional ‘anonymization’ and redaction methods and standards alone to protect the confidentiality of individuals. Generally, one size does not fit all, and tiered modes of access – including public access to privacy-protected data and vetted access to the full data collected – should be provided.
Such access requires thoughtful analysis with expert consultation to evaluate the sensitivity of the data collected and risks of re-identification and to design useful and safe release mechanisms.
Big Data & Privacy -- Response to White House OSTPMicah Altman
Big data has huge implications for privacy, as summarized in our commentary below:
Both the government and third parties have the potential to collect extensive (sometimes exhaustive), fine grained, continuous, and identifiable records of a person’s location, movement history, associations and interactions with others, behavior, speech, communications, physical and medical conditions, commercial transactions, etc. Such “big data” has the ability to be used in a wide variety of ways, both positive and negative. Examples of potential applications include improving government and organizational transparency and accountability, advancing research and scientific knowledge, enabling businesses to better serve their customers, allowing systematic commercial and non-commercial manipulation, fostering pervasive discrimination, and surveilling public and private spheres.
On January 23, 2014, President Obama asked John Podesta to develop in 90 days, a 'comprehensive review' on big data and privacy.
This lead to a series of workshop on big data and technology at MIT, and on social cultural & ethical dimensions at NYU, with a third planned to discuss legal issues at Berkeley. A number of colleagues from our Privacy Tools for Research project and from the BigData@CSAIL projects have contributed to these workshops and raised many thoughtful issues (and the workshop sessions are online and well worth watching).
My colleagues at the Berkman Center, David O'Brien, Alexandra Woods, Salil Vadhan and I have submitted responses to these questions that outline a broad, comprehensive, and systematic framework for analyzing these types of questions and taxonomize a variety of modern technological, statistical, and cryptographic approaches to simultaneously providing privacy and utility. This comment is made on behalf of the Privacy Tools for Research Project, of which we are a part, and has benefitted from extensive commentary by the other project collaborators.
1)Health data is sensitive and confidential; hence, it should .docxteresehearn
1)
Health data is sensitive and confidential; hence, it should be kept safe. Data security is one of the critical activities which has become challenging for many organizations (Frith, 2019). Due to technology advancements, people can save their health data online. Similarly, people are also able to share data with close friends or any other person of interest. Using online platforms to store the data has brought a lot of benefits. The primary benefit is the fact that individuals can share data with medical experts easily. By, this the medical experts will be able to assist the sick people if possible. The data is always accessible as long as one is authorized.
I read different articles that shared information concerning health data breaches. Various health organizations have been affected by data breaches (Garner, 2017). A good example is the University of Washington Medicine. This organization reported that 974,000 patients' data was affected. The attack was noticed by a patient who found some files containing personal information on public sites. The patient then notified the organization, which claimed that some employees made some errors, which led to the leakage. The files were accessible through Google, so the organization had to ask Google to remove the data. Fortunately, the files were removed from the search list, and this occurred in January 2019.
It was risky to let the files containing personal information available on the website (Ronquillo, Erik Winterholler, Cwikla, Szymanski & Levy, 2018). The organization was lucky that the data breach was not significant, and hence, the patients were not significantly affected. It is good to ensure that files containing health data are handled carefully to avoid some problems. In keeping the health data secure, it is good to ensure that the systems are well-protected. The systems can be protected by making use of firewalls which prevent unauthorized people from accessing them. During the data sharing process, a health organization should ensure that the information is encrypted. Encryption prevents unauthorized people from understanding the message that is being shared using different channels. Users should make sure that they use strong passwords.
2)
Protection of patient’s information is the top most priority of health care providers and professionals. Patient’s health information contains personal data and their health conditions hence the federal laws requires to maintain security and privacy to safeguards health information. Privacy, as distinct from confidentiality, is viewed as the right of the individual client or patient to be let alone and to make decisions about how personal information is shared (Brodnik, 2012). Health data is usually stored on paper or electronically, in both these ways it is important to respect the privacy of the patients and hence follow policies to maintain security and privacy rules.
The Health Insurance Portability and Accountabili.
Security, Confidentiality and Privacy in Health of Healthcare Dataijtsrd
Background One of the most important facts that should be considered is confidentiality in order to maintain privacy turning out to be matters of security. Keeping up confidentiality is a crucial factor in any field, as well as health realms. Professionals who have the ingress to approach the patients' communications must keep confidentiality in health. The priority for any human being is privacy to information especially related to health. Security enables us to live peacefully, without anxiety and in full insurance. Methods The interpretive methodology was used in this research as it gives an impression of face to face interactions in healthcare bringing in social reality of what is happening in the health society.Results In consultations on gathering these results for our research, we also realized that the most common threats of loss of data and theft come under certain types of disclosures mainly third parties, routine and inadvertent. Upon this realization, there must be notification to protect security, confidentiality and privacy when security breaches occur mainly to patients. As a result, patients must provide consent about their medical information in electronically form or in writing and the consent must be signed by the patient or family member or trusted entity. The patients must come clear on the nature of the information to be disclosed and where it should be disclosed and also when the consent should expire. At the same time, a health facility must take care of the institution's database and can only disclose to the management of the health institution whose obligation would also be to protect the data, as they might need the information for research purposes, where the researchers have approval from their institution's or to legal representatives.Conclusion The advent of the hype of electronic information technology leads to major inconvenience in the main areas of human life. This manuscript explores issues in maintaining confidentiality and privacy in healthcare and other analysis of its value to individual and society as a whole. “Right to privacy is really important. You pull that brick out and another and pretty soon the house falls.†Tim Cook 2016 1 Jomin George | Takura Bhila ""Security, Confidentiality and Privacy in Health of Healthcare Data"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23780.pdf
Paper URL: https://www.ijtsrd.com/medicine/other/23780/security-confidentiality-and-privacy-in-health-of-healthcare-data/jomin-george
A Review Study on the Privacy Preserving Data Mining Techniques and Approaches14894
This document summarizes privacy preserving data mining techniques. It begins by explaining the need for privacy preserving techniques due to the sensitivity of individual data being mined from large databases. It then classifies privacy preserving techniques based on the data mining scenario, tasks, data distribution, data types, privacy definitions, and protection methods used. Several privacy preserving techniques are described in detail, including data modification techniques like data swapping, aggregation, suppression, and noise addition. Secure multiparty computation techniques that encrypt distributed data sets are also discussed. The document concludes by evaluating these techniques based on their versatility, disclosure risks, information loss, and computational costs.
Anonos NTIA Comment Letter letter on ''Big Data'' Developments and How They I...Ted Myerson
Read our NTIA comment letter on ''Big Data'' Developments and How They Impact the Consumer Privacy Bill of Rights. Filed with the NTIA on August 5, 2014.
Anonos has been working for over two years on technology that transforms data at the data element level enabling de-identification and functional obscurity that preserves the value of underlying data. Specifically, Anonos de-identification and functional obscurity risk management tools help to enable data subjects to share information in a controlled manner, enabling them to receive information and offerings truly personalized for them, while protecting misuse of their data; and to facilitate improved healthcare, medical research and personalized medicine by enabling aggregation of patient level data without revealing the identity of patients.
Due 614 18 10 p.m EST500 words discussion APA not including mi.docxastonrenna
Due 6/14/ 18 10 p.m EST
500 words discussion APA not including min 4 references
Discussion: Ethics and Public Health Data
Public health officials have an obligation to protect both the individual and the “greater good” of the community. This dual mandate can produce situations in which ethically sound decision making is ambiguous. For instance, during the monitoring of many diseases and chronic illnesses, data are shared among multiple agencies for the sake of obtaining a more inclusive data set. Individuals may feel that their privacy is being encroached upon when their personal information is shared among these agencies. As our capacity to access and link data from various disparate sources is enhanced, the security of one’s personal and identifying information is diminished. Indeed, there are frequent reports in the news of data security breaches with potentially devastating consequences for consumers and/or patients.
Post
a brief explanation of what you consider to be the ethical considerations inherent in sharing health data. Then, state your position on whether it is more important, from an ethical standpoint, to protect an individual’s identity or protect the community’s health. Justify your response. Include disease surveillance and informatics examples.
...
This document discusses 4 key challenges for healthcare entities in harnessing big data and analytics: 1) Gaining access to different data streams from other organizations due to fragmented healthcare systems, technological hurdles, and strategic/privacy concerns. 2) Ensuring high quality data by addressing inconsistencies across data sources. 3) Developing robust algorithms and analytics that are supervised by domain experts and can evolve over time. 4) Investing in analytics solutions that are flexible and scalable given the rapidly changing technological environment.
A survey on privacy preserving data publishingijcisjournal
Data mining is a computational process of analysing and extracting the data from large useful datasets. In
recent years, exchanging and publishing data has been common for their wealth of opportunities. Security,
Privacy and data integrity are considered as challenging problems in data
mining.Privacy is necessary to protect people’s interest in competitive situations. Privacy is an abilityto
create and maintain different sort of social relationships with people. Privacy Preservation is one of the
most important factor for an individual since he should not embarrassed by an adversary. The Privacy
Preservation is an important aspect of data mining to ensure the privacy by various methods. Privacy
Preservation is necessary to protect sensitive information associated with individual. This paper provides a
survey of key to success and an approach where individual’s privacy would to be non-distracted.
Legal and ethical considerations in nursing informaticsAHMED ZINHOM
This document outlines key concepts in nursing informatics related to information security, privacy, and ethics. It defines terms like privacy, confidentiality, consent and discusses threats to security like hackers. It also covers security measures to protect information like firewalls and passwords. Specific issues around internet technology, mobile devices and the impact on health information security are examined. Ten security principles related to accountability, consent and challenges to compliance are also overviewed.
Implementation of Data Privacy and Security in an Online Student Health Recor...Kato Mivule
Kato Mivule, Stephen Otunba, Tattwamasi Tripathy, Sharad and Sharma, "Implementation of Data Privacy and Security in an Online Student Health Records System", Proceedings at the ISCA 21th Int Conf on Software Engineering and Data Engineering (SEDE-2012), Pages 143-148, Los Angeles, CA, USA
Anonymizing and Confidential Databases for Privacy Protection Using Suppressi...Editor IJCATR
The technique of k-anonymization has been proposed in the literature as an alternative way to release public information, while ensuring both data privacy and data confidentiality. “X” owns a k-anonymous database and needs to determine whether “X” database, when inserted with a tuple owned by “Y”, is still k-anonymous. Clearly, allowing “X” to directly read the contents of the tuple breaks the privacy of “Y”. In this place,”Y” not get the privacy of own information because the information of “Y” can be accessed by “X” without the prior knowledge of “Y”. On the other hand, the confidentiality of the database managed by “X” is violated once “Y” has access to the contents of database. Thus, the problem is to check whether the database inserted with the tuple is still k-anonymous, without letting “X” and “Y” knows the contents of the tuple and database respectively. In this paper, we propose two protocols solving this problem that is suppression-Based & Generalization-Based k-anonymous and confidential databases using through prototype architecture. And also those two protocols maintain privacy and confidential information in k-anonymous database.
Multilevel Privacy Preserving by Linear and Non Linear Data DistortionIOSR Journals
This document discusses privacy-preserving techniques for data mining called multilevel privacy preserving. It introduces the concept of generating multiple perturbed copies of data at different trust levels to protect privacy while allowing useful data mining. Key techniques discussed include data perturbation through adding random noise or distorting values, as well as data modification through aggregation, suppression, and swapping. Maintaining privacy is achieved by ensuring the noise added to different copies has a "corner-wave" covariance structure so statistical values do not differ significantly from the original data.
Data Anonymization Process Challenges and Context Missionsijdms
Data anonymization is one of the solutions allowing companies to comply with the GDPR directive in terms of data protection. In this context, developers must follow several steps in the process of data anonymization in development and testing environments. Indeed, real personal and sensitive data must not leave the production environment which is very secure. Often, anonymization experts are faced with difficulties including the lack of data flows and mapping between data sources, the non-cooperation of the database project teams (refusal to change) or even the lack of skills of these teams present due to the age of the systems developed by experienced teams who unfortunately left the project. Other problems are lack of data models. The aim of this paper is to discuss an anonymization process of databases of banking applications and present our context-based recommendations to overcome the different issues met and the solutions to improve methodologies of data anonymization process.
This document provides a technical review of secure banking using RSA and AES encryption methodologies. It discusses how RSA and AES are commonly used encryption standards for secure data transmission between ATMs and bank servers. The document first provides background on ATM security measures and risks of attacks. It then reviews related work analyzing encryption techniques. The document proposes using a one-time password in addition to a PIN for ATM authentication. It concludes that implementing encryption standards like RSA and AES can make transactions more secure and build trust in online banking.
This document analyzes the performance of various modulation schemes for achieving energy efficient communication over fading channels in wireless sensor networks. It finds that for long transmission distances, low-order modulations like BPSK are optimal due to their lower SNR requirements. However, as transmission distance decreases, higher-order modulations like 16-QAM and 64-QAM become more optimal since they can transmit more bits per symbol, outweighing their higher SNR needs. Simulations show lifetime extensions up to 550% are possible in short-range networks by using higher-order modulations instead of just BPSK. The optimal modulation depends on transmission distance and balancing the energy used by electronic components versus power amplifiers.
This document provides a review of mobility management techniques in vehicular ad hoc networks (VANETs). It discusses three modes of communication in VANETs: vehicle-to-infrastructure (V2I), vehicle-to-vehicle (V2V), and hybrid vehicle (HV) communication. For each communication mode, different mobility management schemes are required due to their unique characteristics. The document also discusses mobility management challenges in VANETs and outlines some open research issues in improving mobility management for seamless communication in these dynamic networks.
This document provides a review of different techniques for segmenting brain MRI images to detect tumors. It compares the K-means and Fuzzy C-means clustering algorithms. K-means is an exclusive clustering algorithm that groups data points into distinct clusters, while Fuzzy C-means is an overlapping clustering algorithm that allows data points to belong to multiple clusters. The document finds that Fuzzy C-means requires more time for brain tumor detection compared to other methods like hierarchical clustering or K-means. It also reviews related work applying these clustering algorithms to segment brain MRI images.
1) The document simulates and compares the performance of AODV and DSDV routing protocols in a mobile ad hoc network under three conditions: when users are fixed, when users move towards the base station, and when users move away from the base station.
2) The results show that both protocols have higher packet delivery and lower packet loss when users are either fixed or moving towards the base station, since signal strength is better in those scenarios. Performance degrades when users move away from the base station due to weaker signals.
3) AODV generally has better performance than DSDV, with higher throughput and packet delivery rates observed across the different user mobility conditions.
This document describes the design and implementation of 4-bit QPSK and 256-bit QAM modulation techniques using MATLAB. It compares the two techniques based on SNR, BER, and efficiency. The key steps of implementing each technique in MATLAB are outlined, including generating random bits, modulation, adding noise, and measuring BER. Simulation results show scatter plots and eye diagrams of the modulated signals. A table compares the results, showing that 256-bit QAM provides better performance than 4-bit QPSK. The document concludes that QAM modulation is more effective for digital transmission systems.
The document proposes a hybrid technique using Anisotropic Scale Invariant Feature Transform (A-SIFT) and Robust Ensemble Support Vector Machine (RESVM) to accurately identify faces in images. A-SIFT improves upon traditional SIFT by applying anisotropic scaling to extract richer directional keypoints. Keypoints are processed with RESVM and hypothesis testing to increase accuracy above 95% by repeatedly reprocessing images until the threshold is met. The technique was tested on similar and different facial images and achieved better results than SIFT in retrieval time and reduced keypoints.
This document studies the effects of dielectric superstrate thickness on microstrip patch antenna parameters. Three types of probes-fed patch antennas (rectangular, circular, and square) were designed to operate at 2.4 GHz using Arlondiclad 880 substrate. The antennas were tested with and without an Arlondiclad 880 superstrate of varying thicknesses. It was found that adding a superstrate slightly degraded performance by lowering the resonant frequency and increasing return loss and VSWR, while decreasing bandwidth and gain. Specifically, increasing the superstrate thickness or dielectric constant resulted in greater changes to the antenna parameters.
This document describes a wireless environment monitoring system that utilizes soil energy as a sustainable power source for wireless sensors. The system uses a microbial fuel cell to generate electricity from the microbial activity in soil. Two microbial fuel cells were created using different soil types and various additives to produce different current and voltage outputs. An electronic circuit was designed on a printed circuit board with components like a microcontroller and ZigBee transceiver. Sensors for temperature and humidity were connected to the circuit to monitor the environment wirelessly. The system provides a low-cost way to power remote sensors without needing battery replacement and avoids the high costs of wiring a power source.
1) The document proposes a model for a frequency tunable inverted-F antenna that uses ferrite material.
2) The resonant frequency of the antenna can be significantly shifted from 2.41GHz to 3.15GHz, a 31% shift, by increasing the static magnetic field placed on the ferrite material.
3) Altering the permeability of the ferrite allows tuning of the antenna's resonant frequency without changing the physical dimensions, providing flexibility to operate over a wide frequency range.
This document summarizes a research paper that presents a speech enhancement method using stationary wavelet transform. The method first classifies speech into voiced, unvoiced, and silence regions based on short-time energy. It then applies different thresholding techniques to the wavelet coefficients of each region - modified hard thresholding for voiced speech, semi-soft thresholding for unvoiced speech, and setting coefficients to zero for silence. Experimental results using speech from the TIMIT database corrupted with white Gaussian noise at various SNR levels show improved performance over other popular denoising methods.
This document reviews the design of an energy-optimized wireless sensor node that encrypts data for transmission. It discusses how sensing schemes that group nodes into clusters and transmit aggregated data can reduce energy consumption compared to individual node transmissions. The proposed node design calculates the minimum transmission power needed based on received signal strength and uses a periodic sleep/wake cycle to optimize energy when not sensing or transmitting. It aims to encrypt data at both the node and network level to further optimize energy usage for wireless communication.
This document discusses group consumption modes. It analyzes factors that impact group consumption, including external environmental factors like technological developments enabling new forms of online and offline interactions, as well as internal motivational factors at both the group and individual level. The document then proposes that group consumption modes can be divided into four types based on two dimensions: vertical (group relationship intensity) and horizontal (consumption action period). These four types are instrument-oriented, information-oriented, enjoyment-oriented, and relationship-oriented consumption modes. Finally, the document notes that consumption modes are dynamic and can evolve over time.
The document summarizes a study of different microstrip patch antenna configurations with slotted ground planes. Three antenna designs were proposed and their performance evaluated through simulation: a conventional square patch, an elliptical patch, and a star-shaped patch. All antennas were mounted on an FR4 substrate. The effects of adding different slot patterns to the ground plane on resonance frequency, bandwidth, gain and efficiency were analyzed parametrically. Key findings were that reshaping the patch and adding slots increased bandwidth and shifted resonance frequency. The elliptical and star patches in particular performed better than the conventional design. Three antenna configurations were selected for fabrication and measurement based on the simulations: a conventional patch with a slot under the patch, an elliptical patch with slots
1) The document describes a study conducted to improve call drop rates in a GSM network through RF optimization.
2) Drive testing was performed before and after optimization using TEMS software to record network parameters like RxLevel, RxQuality, and events.
3) Analysis found call drops were occurring due to issues like handover failures between sectors, interference from adjacent channels, and overshooting due to antenna tilt.
4) Corrective actions taken included defining neighbors between sectors, adjusting frequencies to reduce interference, and lowering the mechanical tilt of an antenna.
5) Post-optimization drive testing showed improvements in RxLevel, RxQuality, and a reduction in dropped calls.
This document describes the design of an intelligent autonomous wheeled robot that uses RF transmission for communication. The robot has two modes - automatic mode where it can make its own decisions, and user control mode where a user can control it remotely. It is designed using a microcontroller and can perform tasks like object recognition using computer vision and color detection in MATLAB, as well as wall painting using pneumatic systems. The robot's movement is controlled by DC motors and it uses sensors like ultrasonic sensors and gas sensors to navigate autonomously. RF transmission allows communication between the robot and a remote control unit. The overall aim is to develop a low-cost robotic system for industrial applications like material handling.
This document reviews cryptography techniques to secure the Ad-hoc On-Demand Distance Vector (AODV) routing protocol in mobile ad-hoc networks. It discusses various types of attacks on AODV like impersonation, denial of service, eavesdropping, black hole attacks, wormhole attacks, and Sybil attacks. It then proposes using the RC6 cryptography algorithm to secure AODV by encrypting data packets and detecting and removing malicious nodes launching black hole attacks. Simulation results show that after applying RC6, the packet delivery ratio and throughput of AODV increase while delay decreases, improving the security and performance of the network under attack.
The document describes a proposed modification to the conventional Booth multiplier that aims to increase its speed by applying concepts from Vedic mathematics. Specifically, it utilizes the Urdhva Tiryakbhyam formula to generate all partial products concurrently rather than sequentially. The proposed 8x8 bit multiplier was coded in VHDL, simulated, and found to have a path delay 44.35% lower than a conventional Booth multiplier, demonstrating its potential for higher speed.
This document discusses image deblurring techniques. It begins by introducing image restoration and focusing on image deblurring. It then discusses challenges with image deblurring being an ill-posed problem. It reviews existing approaches to screen image deconvolution including estimating point spread functions and iteratively estimating blur kernels and sharp images. The document also discusses handling spatially variant blur and summarizes the relationship between the proposed method and previous work for different blur types. It proposes using color filters in the aperture to exploit parallax cues for segmentation and blur estimation. Finally, it proposes moving the image sensor circularly during exposure to prevent high frequency attenuation from motion blur.
This document describes modeling an adaptive controller for an aircraft roll control system using PID, fuzzy-PID, and genetic algorithm. It begins by introducing the aircraft roll control system and motivation for developing an adaptive controller to minimize errors from noisy analog sensor signals. It then provides the mathematical model of aircraft roll dynamics and describes modeling the real-time flight control system in MATLAB/Simulink. The document evaluates PID, fuzzy-PID, and PID-GA (genetic algorithm) controllers for aircraft roll control and finds that the PID-GA controller delivers the best performance.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
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Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Azure Interview Questions and Answers PDF By ScholarHat
G0953643
1. IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 9, Issue 5 (Mar. - Apr. 2013), PP 36-43
www.iosrjournals.org
Privacy Preservation for Knowledge Discovery: A Survey
Jalpa Shah1, Mr. Vinit kumar Gupta2
1
Department of Computer Engineering, Hasmukh Goswami College of Engineering, Vehlal, Gujarat
2
Department of Computer Engineering, Hasmukh Goswami College of Engineering, Vehlal, Gujarat
Abstract: Today’s globally networked society places great demand on the dissemination and sharing of
information. Privacy Preservation is an important issue in the release of data for mining purposes. How to
efficiently protect individual privacy in data publishing is especially critical. With releasing of microdata such
as social security number disease by some organization should contain privacy in data publishing. Data holders
can remove explicit identifiers to gain privacy but other attributes which are in published data can lead to
reveal privacy to adversary. So several methods such as K-anonymity, L-diversity, T-closeness, (n,t) closeness,
(α,k)-anonymization, p-sensitive k-anonymity and others method come into existence to maintain privacy in data
publishing.
Keywords – Data anonymization, Generalization, Data suppression
I. INTRODUCTION
There are so many organizations who publish their data in various forms. These forms contain various
information. Information can be helpful for someone and at the same time can be useless for another one. Some
information may be important for business point of view, industrial point of view that depends on person to
person. So which information is sensitive i.e. we do not want to disclose it for general people and which
information can be published. So caring of these issues, organization needs to publish their information. As for
example in a hospital system a lot of patient comes for their treatment in respective departments. Hospital need
to maintain their records and make a file for that which contains patient information. They want to publish
reports such that information remains practically useful and the important thing is that identity of an individual
can not be determined. So publishing of data is main concern here. Organization needs to publish microdata.
Microdata e.g. Medical data, voter registration and census data for research and other purposes. These data are
stored in a table. Each record corresponds to one individual. Microdata is a valuable source of information for
the allocation of public funds, medical research, and trend analysis. However, if individuals can be uniquely
identified in the microdata then their private information (such as their medical condition) would be disclosed,
and this is unacceptable. Each record has number of attributes, which can be divided into three categories. (1)
Explicit identifiers attributes that clearly identify an individual. E.g. - social security number. (2) Quasi-
identifiers attributes whose value when taken together can identify an identify an individual.e.g. Zip-code, birth
date and gender. (3) Attributes those are sensitive such as disease and salary. It is necessary to protect sensitive
information of individuals from being disclosed. There are two types of information disclosure identity
disclosure and attribute disclosure [9]. Identity disclosure occurs when an individual is linked to a particular
record in the released table. Attribute disclosure occurs when new information about some individuals is
revealed, i.e., the released data makes it possible to infer the characteristics of an individual more accurately
than it would be possible before the data release. If there is only one female black dentist is in area and sequence
queries reveal that she is in database then identification occurs. Identity disclosure often leads to attribute
disclosure. Once there is identity disclosure, an individual is re-identified and the corresponding sensitive values
are revealed. Attribute disclosure can occur with or without identity disclosure. While the released table gives
useful information to researchers, it presents disclosure risk to the individuals whose data are in the table.
Therefore, to limit the disclosure risk to an acceptable level while maximizing the benefit. This can be done by
anonymizing the data before release. By knowing the quasi identifiers can lead to know the sensitive
information. This can be done by knowing the individual personally or other publicly available database.
1.1 Motivation
Huge databases exist in the society example medical data, census data, data gathered by government
agencies. The rapid evolution of storage, processing and computational technologies is changing the traditional
information system architecture adopted both private companies and public organization. This change is
necessary for two reasons First the amount of information held by organization is increasing very quickly thanks
to large storage capacity and computational power of modern devices. Second the data collected by organization
contains sensitive information (e.g. identifying information, financial data, and health diagnosis) whose
confidentiality must be preserved. More and more healthcare system collect sensitive information about
www.iosrjournals.org 36 | Page
2. Privacy Preservation for Knowledge Discovery: A Survey
historical and present hospitalization, and more in general health condition of patients. Since these data are
associated with identity of patients. As a consequence, any healthcare system should adopt an adequate privacy
protection system, which guarantees the protection of sensitive attribute. Companies and agencies that collect
such data often need to publish and share the data for research and other purposes. However, such data usually
contains personal sensitive information, the disclosure of which may violate the individual's privacy.
II. Theoretical Background And Review
2.1 Theoretical background
Some important terms regarding data preservation for data publishing
2.1.1 Information Disclosure Risk
When releasing microdata, it is necessary to prevent the sensitive information of the individuals from
being disclosed. Three types of information disclosure have been identified in the literature [7–9]: membership
disclosure, identity disclosure, and attribute disclosure. Membership Disclosure When the data to be published
is selected from a larger population and the selection criteria are sensitive (e.g., when publishing datasets about
diabetes patients for research purposes), it is important to prevent an adversary from learning whether an
individual's record is in the data or not. Identity Disclosure Identity disclosure (also called re-identification)
occurs when an individual is linked to a particular record in the released data. Identity disclosure is what the
society views as the clearest form of privacy violation. If one is able to correctly identify one individual's record
from supposedly anonymized data, then people agree that privacy is violated. In fact, most publicized privacy
attacks are due to identity disclosure. In the case of GIC medical database [4], Sweeney reidentified the medical
record of the state governor of Massachusetts. In the case of AOL search data [5], the journalist from New York
Times linked AOL searcher NO. 4417749 to Thelma Arnold, a 62-year-old widow living in Lilburn, GA. And in
the case of Netflix prize data, researchers demonstrated that an adversary with a little bit of knowledge about an
individual subscriber can easily identify this subscriber's record in the data. When identity disclosure occurs, it
has been said that “anonymity” is broken. Attribute Disclosure. Attribute disclosure occurs when new
information about some individuals is revealed, i.e., the released data makes it possible to infer the
characteristics of an individual more accurately than it would be possible before the data release. Identity
disclosure often leads to attribute disclosure. Once there is identity disclosure, an individual is re-identified and
the corresponding sensitive values are revealed. Attribute disclosure can occur with or without identity
disclosure. It has been recognized that even disclosure of false attribute information may cause harm [8]. An
observer of the released data may incorrectly perceive that an individual's sensitive attribute takes a particular
value and behave accordingly based on the perception. This can harm the individual, even if the perception is
incorrect. In some scenarios, the adversary is assumed to know who is and who is not in the data, i.e., the
membership information of individuals in the data. The adversary tries to learn additional sensitive information
about the individuals. In these scenarios, our main focus is to provide identity disclosure protection and attribute
disclosure protection. In other scenarios where membership information is assumed to be unknown to the
adversary, membership disclosure should be prevented. Protection against membership disclosure also helps
protect against identity disclosure and attribute disclosure: it is in general hard to learn sensitive information
about an individual if you don't even know whether this individual's record is in the data or not.
2.1.2 Data Anonymization
While the released data gives useful information to researchers, it presents disclosure risk to the
individuals whose data are in the data. Therefore, our objective is to limit the disclosure risk to an acceptable
level while maximizing the benefit. This is achieved by anonymizing the data before release. The first step of
anonymization is to remove explicit identifiers. However, this is not enough, as an adversary may already know
the quasi-identifier values of some individuals in the table. This knowledge can be either from personal
knowledge (e.g., knowing a particular individual in person), or from other publicly-available databases (e.g., a
voter registration list) that include both explicit identifiers and quasi-identifiers. Privacy attacks that use quasi-
identifi ers to re-identify an individual's record from the data is also called re-identification attacks .To prevent
re-identification attacks, further anonymization is required. A common approach is generalization, which
replaces quasi-identifier values with values that are less-specific but semantically consistent. For example, age
24 can be generalized to an age interval [20 − 29]. As a result, more records will have the same set of quasi-
identifier values. We define a QI group to be a set of records that have the same values for the quasi- identifiers.
In other words, a QI group consists of a set of records that are indistinguishable from each other from their
quasi-identifiers.
www.iosrjournals.org 37 | Page
3. Privacy Preservation for Knowledge Discovery: A Survey
2.2 Review
Many researchers have found several approaches for data preservation for data publishing. So several
methods such K-anonymity, L-diversity, T-closeness and others are come into existence to maintain privacy in
data publishing. In this paper we discussed pros and cons of all these techniques.
2.2.1 K-anonymity
Samarati and Sweeney [10], [11] proposed a definition of privacy called k-anonymity. Each record is
indistinguishable with at least k-1 other records with respect to the quasi-identifier. In other words, k-anonymity
requires that each equivalence class contains at least k records. So there is need to hide the information. So to
remove these anomalies k-anonymity is an approach in this direction. The attacker can join the non-sensitive
data to identify the sensitive attribute. Generalization and suppression are the techniques of anoymization. In
generalization replace the original value by a semantically consistent but less specific value. In suppression data
not released at all it is suppressed on cell level or tuple level. Figure 1(a) shows medical records from a hospital
located in New York. This table does not contain uniquely identifying attributes like name, social security
number, etc. In this example, division of the attributes into two groups: the sensitive attributes (consisting only
of medical condition) and the non-sensitive attributes (zip code, age, and nationality). An attribute is marked
sensitive if an adversary must not be allowed to discover the value of that attribute for any individual in the
dataset.
TABLE-1 Inpatient Microdata
Non sensitive Sensitive
Zip code Age Nationality Condition
1 13053 28 Nepali Heart disease
2 13068 29 Japanese Viral
3 13068 21 Nepali Viral
4 14853 50 Chinese Cancer
5 14853 55 Indian Viral
6 14850 47 Chinese Viral
7 13053 31 Nepali Cancer
8 13053 37 Nepali Cancer
9 13068 36 Indian Cancer
Here the collection of attributes {zip code, age, nationality} is the quasi-identifier for this dataset. Figure 3
represents a 3-anonymous table derived from the table in Figure 2(a) Here “*” denotes a suppressed value so,
for example, “zip code = 1485*” means that the zip code is in the range [14850−14859] and “age=3*”means the
age is in the range [30-39]. Suppression is done by cell level or tuple level. Here there are 9 records in this table.
So 3-anonymous table would be having three equivalence classes. In this 3-anonymous table, each tuple has the
same values for the quasi-identifier as at least three other tuples in the table. So k-anonymity protects against
identity disclosure.
TABLE-2 3-anonymous Inpatient Microdata
Non Sensitive Sensitive
Zip code Age Nationality Condition
1 130** < 30 * Heart
disease
2 130** < 30 * Viral
3 130** < 30 * Viral
4 140** > 40 * Cancer
5 140** > 40 * Viral
6 140** > 40 * Viral
7 130** 3* * Cancer
8 130** 3* * Cancer
9 130** 3* * Cancer
2.2.2 Homogeneity attack
K-anonymity suffers from homogeneity attack [4] can create groups that leak information due to lack
of diversity in the sensitive attribute. From the previous example Alice and Bob are neighbours. One day Bob
falls ill and is taken by ambulance to the hospital. After seeing the ambulance, Alice wants to know what kind of
www.iosrjournals.org 38 | Page
4. Privacy Preservation for Knowledge Discovery: A Survey
disease Bob is suffering from. Alice can see the 3-anonymous table of current inpatient records published by the
hospital which is shown in the previous figure, and so she can know that one of the records in this table contains
Bob’s data. Since Alice is Bob’s neighbour, she knows that Bob is a 31-year-old American male who lives in
the zip code 13053. Therefore, Alice knows that Bob’s record number is 7, 8 or 9 Now, all of those patients
have the same medical condition (cancer), and so Alice concludes that Bob has cancer. This is not uncommon
consider a dataset containing 60,000 distinct tuples where the sensitive attribute can take 3 distinct values and is
not correlated with the non sensitive attributes. 5-anonymization of this table will have around 12,000 groups
and, on average, 1 out of every 81 groups will have no diversity (the values for the sensitive attribute will all be
the same). Thus we should expect about 148 groups with no diversity. Therefore, information about 740 people
would be compromised by a homogeneity attack. This suggests that in addition to k-anonymity, the sanitized
table should also ensure “diversity” all tuples that share the same values of their quasi-identifiers should have
diverse values for their sensitive attributes.
2.2.3 Background knowledge attack
K-anonymity does not protect against attacks based on background knowledge. Alice has a closed
friend named Umeko who is admitted to the same hospital as Bob, and whose patient records also appear in the
table shown in Figure 2. Alice knows that Umeko is a 21 year old Japanese female who currently lives in zip
code 13068. Based on this information, Alice learns that Umeko’s information is contained in record number 1,
2 or 3 without additional information; Alice is not sure whether Umeko caught a virus or has heart disease.
However, it is well known that Japanese have an extremely low incidence of heart disease. Therefore Alice
concludes with near certainty that Umeko has a viral infection. After knowing the background knowledge an
intruder can identify the sensitive attributes. So k-anonymity does not protect against homogeneity attack and
background knowledge attack. It protects against identity disclosure very well but failed to protect against
attribute disclosure.
2.2.4 L-Diversity
To address the limitation of K-anonymity Machanavajjhala [4] an equivalence class is said to have l -
diversity if there are at least “well represented” values for the sensitive attribute. A table is said to have l-
diversity if every equivalence class of the table has l-diversity. The meaning of well represented would be to
ensure there are at least l-distinct values for the sensitive attribute in each equivalence class. Distinct l-diversity
does not prevent probabilistic inference attacks. An equivalence class may have one value appear much more
frequently than other values, enabling an adversary to conclude that an entity in the equivalence class is very
likely to have that value. This leads to the development of stronger notions of l-diversity.
2.2.5 Similarity attack
When the sensitive attribute values in an equivalence class are distinct but semantically similar, an
adversary can learn important information. For example consider this example [7], this is the original table and
next figure [7] shows anonymized version which satisfy distinct and entropy 3-diversity. Here there are two
sensitive attributes salary and disease. Suppose sum one knows that Bob’s record corresponds to one of the first
three records, then one knows that Bob’s Salary is in the range [3K–5K] and can infer that Bob’s salary is
relatively low. This attack applies not only to numeric attributes like salary, but also to categorical attributes like
disease. Knowing that Bob’s record belongs to the first equivalence class enables one to conclude that Bob has
some stomach-related problems, because all three diseases in the class are stomach-related. So here sensitive
information is revealed. So l-diversity also has certain limitation. It does not well protect attribute disclosure
very well.
TABLE-3 Original Salary/Disease table [7]
Non sensitive Sensitive
Zip code Age Salary Disease
1 47677 29 3k Gastric ulcer
2 47602 22 4k Gastritis
3 47678 27 5k Stomach cancer
4 47905 43 6k Gastritis
5 47909 52 11k Flu
6 47606 47 8k Bronchitis
7 47675 30 7k Bronchitis
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5. Privacy Preservation for Knowledge Discovery: A Survey
8 47603 36 9k Pneumonia
9 47607 32 10k Stomach cancer
TABLE-4 3-Diverse Version of Original Table [7]
Non sensitive Sensitive
Zip code Age Salary Disease
1 476** 2* 3k Gastric ulcer
2 476** 2* 4k Gastric
3 476** 2* 5k Stomach cancer
4 4790* >40 6k Gastritis
5 4790* >40 11k Flu
6 4790* >40 8k Bronchitis
7 476** 3* 7k Bronchitis
8 476** 3* 9k Pneumonia
9 476** 3* 10k Stomach cancer
2.2.5 T-Closeness [3]
L-diversity suffers from similarity attack. L-diversity requires that each equivalence class has at least l
well represented values for each sensitive attribute. Ninghui li, Tiachang li and S. Venkatasubramanian
proposed t-closeness to deal with Similarity attack. T-closeness says that the distribution of a sensitive attribute
in any equivalence class is close to the distribution of the attribute in the overall table. An equivalence class is
said to have t-closeness if the distance between the distribution of a sensitive attribute in this class and the
distribution of the attribute in the whole table is no more then a threshold t. A table is said to have t-closeness if
all equivalence class have t-closeness. T-closeness with EMD handles the difficulties of l-diversity. EMD has
been choosing instead of other distance metric. For numerical attributes ordered distance has been used and for
categorical attributes hierarchical distance formula has been used.
Say Q = { 3k,4k,5k,6k,7k,8k,9k,10k,11k},P1 ={3k,4k,5k} and P2 = {6k,8k,11k}.from the given values of Q,
P1,P2 we can calculate D[P1,Q] and D[P2,Q] using EMD. We have D [P1, Q] = 0.375 and D [P2, Q] = 0.167.
Fig.1. (a) Hierarchical for categorical attribute Disease [4]
For the disease attribute, use of hierarchy as shown in Figure to define the ground distances. For example, the
distance between “Flu” and “Bronchitis” is 1/3, the distance between “Flu” and “Pulmonary embolism” is 2/3,
and the distance between “Flu” and “Stomach cancer” is 3/3 = 1. Then the distance between the distribution
{gastric ulcer, gastritis, stomach cancer} and the overall distribution is 0.5 while the distance between the
distribution {gastric ulcer, stomach cancer, pneumonia} is 0.278. So anonymized version of previous table can
be generated. It has 0.167 closeness w.r.t Salary. The similarity attack is prevented in the following table.
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6. Privacy Preservation for Knowledge Discovery: A Survey
TABLE-60.167 closeness w.r.t salary [3]
Non sensitive Sensitive
Zip code Age Salary Disease
1 4767* < 40 3k Gastric ulcer
2 4767* < 40 4k Stomach cancer
3 4767* < 40 9k Pneumonia
4 4790* > 40 6k Gastritis
5 4790* > 40 11k Flu
6 4790* > 40 8k Bronchitis
7 4760* < 40 4k Gastritis
8 4760* < 40 7k Pneumonia
9 4760* < 40 10k Stomach cancer
For example, Alice can not infer that Bob has a stomach related disease based on this table.
2.2.5 Motivation for (n,t) Closeness [3]
T- Closeness limits the release of useful information through the following example table shown below
that contains 3000 individuals and next table shows an anonymized version of it. The sensitive attribute is
disease. Count is a column that indicates number of individuals. The probability of cancer among the population
in the dataset is 700/3000 = 0.23. While the probability of cancer among individuals among first equivalence
class is 300/600 = 0.5. Since (0.5-0.23 > 0.1) the anonymized table does not satisfy 0.1-closeness.
TABLE-7 Original Patient Table [3]
Zip code Age Disease Count
1 47673 29 Cancer 100
2 47674 21 Flu 100
3 47605 25 Cancer 200
4 47602 23 Flu 200
5 47905 43 Cancer 100
6 47904 48 Flu 900
7 47906 47 Cancer 100
8 47907 41 Flu 900
9 47603 34 Cancer 100
10 47605 30 Flu 100
11 47602 36 Cancer 100
12 47607 32 Flu 100
To achieve 0.1-closeness, all tuples in Table have to be generalized into a single equivalence class. This results
in substantial information loss. If we consider the original patient data in Table, we can see that the probability
of cancer among people living in zipcode 476** is as high as 500/1000 = 0.5. While the probability of cancer
among people living in zipcode 479** is only 200/2000 = 0.1. We can see here people living in zipcode 476**
have a much higher rate of cancer will be hidden if 0.1-closeness is enforced.
TABLE-8 Violating 0.1 Closeness [3]
Zip code Age Disease Count
1 476** 2* Cancer 300
2 476** 2* Flu 300
3 479** 4* Cancer 200
4 479** 4* Flu 1800
5 476** 3* Cancer 200
6 476** 3* Flu 200
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7. Privacy Preservation for Knowledge Discovery: A Survey
2.2.6 (n, t) Closeness [3]
The (n, t) closeness model requires that every equivalence class of a table must contains at least n
records and the distance between the two distributions of the sensitive attribute in the equivalence class is not
more than the threshold t. An equivalence class E1 is said to have (n, t)-closeness if there exists a set E2 of
records that is a natural superset of E1 such that E2 contains at least n records, and the distance between the two
distributions of the sensitive attribute in E1 and E2 is no more than a threshold t. A table is said to have (n, t)-
closeness if all equivalence classes have (n, t)-closeness. In the definition of the (n, t)-closeness, the value of n
defines the size of the viewer’s background knowledge. A smaller n means that the viewer knows the sensitive
information about a smaller group of records. The value of t limits the amount of sensitive information that the
viewer can get from the released table. A smaller t implies a stronger privacy requirement. Choosing the
parameters n and t would affect the level of privacy and utility. The larger n is and the smaller t is, one achieves
more privacy, and less utility. It does not deal with multiple sensitive attributes. Suppose we have two sensitive
attributes like Salary and Disease. One can consider the two attributes separately, i.e., an equivalence class E has
(n, t)-closeness if E has (n, t)-closeness with respect to both Salary and Disease.
2.2.7 (α, k)-anonymization
WONG R C et al. propose an (α, k)-anonymity model to protect both identifications and relationships
to sensitive information in data and to limit the confidence of the implications from the quasi-identifier to a
sensitive value (attribute) to within α. The model avoids the sensitive information is inferred by strong
implications.
(α, k)-ANONYMIZATION: A view of a table is said to be an (α, k)-anonymization of the table if the view
modifies the table such that the view satisfies both k anonymity and α- deassociation properties with respect to
the quasi-identifier [16].
2.2.8 p-sensitive k-anonymity
Traian Marius Truta and Bindu Vinay introduce p sensitive k-anonymity that and protects against both
identity and attribute disclosure on the base of extending k anonymity model.
p-sensitive k-anonymity property: The masked microdata satisfies p-sensitive k anonymity property if it
satisfies k-anonymity, and for each group of tuples with the identical combination of key attribute values that
exists in masked microdata, the number of distinct values for each confidential attribute occurs at least p times
within the same group [17]. P-sensitive k-anonymity protects against attribute disclosure. On this aspect, it is the
same with t-Closeness.
2.3 The Approaches Based on Clustering
Clustering is the problem of partitioning a set of objects into groups, such that objects in the same
group are more similar to each other than objects in other groups based on some defined similarity criteria.
Various approaches based on clustering have been proposed. In [13], the idea of clustering to minimize
information loss and ensure good data quality and formulate a specific clustering problem is proposed. The key
idea is that data records are naturally similar to each other should be part of the same equivalence class. [14]
Achieves anonymity via constraining each cluster must contain no fewer than a pre-specified number of data
records.[15] Introduces a family of geometric data transformation methods (GDTMs) that distort confidential
numerical attributes. The advantage of these proposes based on clustering are high-accurate and available result.
III. Conclusion And Prospect
Privacy here means logical security of data not the traditional security of data. Here adversary uses
legitimate method. K-anonymity is an approach to protect microdata. K-anonymity is done by generalization
and suppression techniques to publish the useful information. L-diversity is one step ahead to k-anonymity. But
it does not protect attribute disclosure very well. T-closeness comes into picture to remove similarity attack.
Some pitfalls in T-closeness leads to (n,t) closeness. We conclude five research directions of privacy preserving
approaches for knowledge discovery by analyzing the existing work in future.
1) The research of finding K-anonymity solution with suppressing fewest cells by reducing complexity.
2) The research of personalized privacy preservation will become an issue.
3) How to improve the efficiency of implementation and ensure available of the result in order to meet
the various requirements.
4) The research about how to combine the advantage of above approaches.
5) The research about improving the algorithm, generalized for both categorical and numerical values.
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8. Privacy Preservation for Knowledge Discovery: A Survey
Acknowledgement
I would be thankful to my guide assistant professor Vinitkumar Gupta here for fervent help when I
have some troubles in paper writing. I will also thank my class mates in laboratory for their concern and support
both in study and life.
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