Seminar Monday March 5th 2018 by BigInsight and Statistics Norway: Presentation by Johan Gustav Bellika. The Norwegian Primary Care Research Network IT infrastructure: The Snow system
A Survey: Privacy Preserving Using Obfuscated Attribute In e-Health Cloudrahulmonikasharma
Cloud computing now a day’s provides numerous number of benefits to their users. As the Cloud infrastructure is not directly under control of user its seems to be difficult for user to have a better security. Other side as the number of user grow even it become more difficult to manage a data such a way that user needs for any data are satisfied efficiently. There are lots of chances to misuse the data of user. So, here Cloud providers need to balance this two fundamental of Privacy handling and efficient analysis of data together is become very important. When we talk about the health records of patient or medical firm and available on remote machine issue of privacy of record provided by the anonymization fundamental. Here various researcher provided a technique T- Closeness to achieve this goal. It also important to provide the security of stored data using obfuscation mechanism . Some time full obfuscation of file consume more time so many researcher provided scheme of attribute based obfuscation which lessen the burden of Cloud server by providing adequate security and also help to execute user query faster. In this paper we aim to provide survey on various fundamental given by the different researcher.
Presentation given by Kate LeMay at the 'Sharing Health-y Data: Challenges and Solutions' workshop, held at The Menzies Research Institute (Hobart, Tasmania) on 28th June 2016. The event was co-hosted by ANDS and the University of Tasmania library
A description of BRISSKit, an open source tool that may be used to combine datasets held in different locations and analyse them for the purpose of research. Talk give by Jonathan Tedds of Leicester Uni. for the Data Management in Practice workshop, which took place on Nov 14th 2013 at the London School of Hygiene and Tropical Medicine
Presentation given by Brian Stokes about the work of the Tasmanian Data Linkage Unit. Given during the 'Sharing Health-y data: Challenges and Solutions' workshop held at the Menzies Research Institute in Hobart, Tasmania, on 28th June 2016.
Intelligent data analysis for medicinal diagnosisIRJET Journal
The document describes a proposed privacy-preserving patient-centric clinical decision support system called PPCD that uses naive Bayesian classification to help doctors predict disease risks for patients in a privacy-preserving manner. PPCD allows medical diagnosis and prediction of disease risks for new patients without leaking any individual patient medical information. It utilizes historical medical information from past patients, stored privately in the cloud, to train a naive Bayesian classifier. This trained classifier can then be used to diagnose diseases for new patients based on their symptoms while preserving privacy. The system also introduces a new aggregation technique called additive homomorphic proxy aggregation to allow training of the naive Bayesian classifier without revealing individual patient medical records.
How Data Commons are Changing the Way that Large Datasets Are Analyzed and Sh...Robert Grossman
Data commons are emerging as a solution to challenges in analyzing and sharing large biomedical datasets. A data commons co-locates data with cloud computing infrastructure and software tools to create an interoperable resource for the research community. Examples include the NCI Genomic Data Commons and the Open Commons Consortium. The open source Gen3 platform supports building disease- or project-specific data commons to facilitate open data sharing while protecting patient privacy. Developing interoperable data commons can accelerate research through increased access to data.
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.
Current trends in data security nursing research pptNursing Path
The document discusses current trends in data security. It begins by defining data security and its goals of confidentiality and integrity. Traditional SQL-based access control and views are described as having limitations. Two main attacks are discussed: SQL injection due to poor application implementation of security policies, and unintended information leakage when published data is combined from multiple sources. Current research topics aim to address leakage, enforce complex privacy policies, and allow secure sharing of data through techniques like encryption and secure computation. The challenges of moving policy implementation closer to the database are also discussed.
A Survey: Privacy Preserving Using Obfuscated Attribute In e-Health Cloudrahulmonikasharma
Cloud computing now a day’s provides numerous number of benefits to their users. As the Cloud infrastructure is not directly under control of user its seems to be difficult for user to have a better security. Other side as the number of user grow even it become more difficult to manage a data such a way that user needs for any data are satisfied efficiently. There are lots of chances to misuse the data of user. So, here Cloud providers need to balance this two fundamental of Privacy handling and efficient analysis of data together is become very important. When we talk about the health records of patient or medical firm and available on remote machine issue of privacy of record provided by the anonymization fundamental. Here various researcher provided a technique T- Closeness to achieve this goal. It also important to provide the security of stored data using obfuscation mechanism . Some time full obfuscation of file consume more time so many researcher provided scheme of attribute based obfuscation which lessen the burden of Cloud server by providing adequate security and also help to execute user query faster. In this paper we aim to provide survey on various fundamental given by the different researcher.
Presentation given by Kate LeMay at the 'Sharing Health-y Data: Challenges and Solutions' workshop, held at The Menzies Research Institute (Hobart, Tasmania) on 28th June 2016. The event was co-hosted by ANDS and the University of Tasmania library
A description of BRISSKit, an open source tool that may be used to combine datasets held in different locations and analyse them for the purpose of research. Talk give by Jonathan Tedds of Leicester Uni. for the Data Management in Practice workshop, which took place on Nov 14th 2013 at the London School of Hygiene and Tropical Medicine
Presentation given by Brian Stokes about the work of the Tasmanian Data Linkage Unit. Given during the 'Sharing Health-y data: Challenges and Solutions' workshop held at the Menzies Research Institute in Hobart, Tasmania, on 28th June 2016.
Intelligent data analysis for medicinal diagnosisIRJET Journal
The document describes a proposed privacy-preserving patient-centric clinical decision support system called PPCD that uses naive Bayesian classification to help doctors predict disease risks for patients in a privacy-preserving manner. PPCD allows medical diagnosis and prediction of disease risks for new patients without leaking any individual patient medical information. It utilizes historical medical information from past patients, stored privately in the cloud, to train a naive Bayesian classifier. This trained classifier can then be used to diagnose diseases for new patients based on their symptoms while preserving privacy. The system also introduces a new aggregation technique called additive homomorphic proxy aggregation to allow training of the naive Bayesian classifier without revealing individual patient medical records.
How Data Commons are Changing the Way that Large Datasets Are Analyzed and Sh...Robert Grossman
Data commons are emerging as a solution to challenges in analyzing and sharing large biomedical datasets. A data commons co-locates data with cloud computing infrastructure and software tools to create an interoperable resource for the research community. Examples include the NCI Genomic Data Commons and the Open Commons Consortium. The open source Gen3 platform supports building disease- or project-specific data commons to facilitate open data sharing while protecting patient privacy. Developing interoperable data commons can accelerate research through increased access to data.
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.
Current trends in data security nursing research pptNursing Path
The document discusses current trends in data security. It begins by defining data security and its goals of confidentiality and integrity. Traditional SQL-based access control and views are described as having limitations. Two main attacks are discussed: SQL injection due to poor application implementation of security policies, and unintended information leakage when published data is combined from multiple sources. Current research topics aim to address leakage, enforce complex privacy policies, and allow secure sharing of data through techniques like encryption and secure computation. The challenges of moving policy implementation closer to the database are also discussed.
Privacy Perserving DataBases, how they are managed, built and secured. with an introduction to main methods of Anonymization techniques, PPDB data mining, P3P and Hippocratic DBs.
Focusing on health verticle consistent with Open Knowledge Networking initiative. Also see a relevant review of: Contextualized Knowledge Graph portal: https://www.slideshare.net/ntkimvinh7/ckg-portal-a-knowledge-publishing-proposal-for-open-knowledge-network
VOLUME-7 ISSUE-8, AUGUST 2019 , International Journal of Research in Advent Technology (IJRAT) , ISSN: 2321-9637 (Online) Published By: MG Aricent Pvt Ltd
Privacy preserving in data mining with hybrid approachNarendra Dhadhal
The document discusses privacy preserving techniques in data mining. It outlines various privacy preserving approaches like randomization, encryption, and anonymization. K-anonymization is described as an important anonymization technique that involves generalization and suppression of data to ensure each record is indistinguishable from at least k-1 other records. The document also reviews several research papers on privacy preserving data mining and discusses issues like homogeneity attacks with k-anonymization. A hybrid approach combining k-anonymization with perturbation is proposed to better protect sensitive data privacy.
Performance Analysis of Hybrid Approach for Privacy Preserving in Data Miningidescitation
Now-a day’s data sharing between two organizations
is common in many application areas like business planning
or marketing. When data are to be shared between parties,
there could be some sensitive data which should not be
disclosed to the other parties. Also medical records are more
sensitive so, privacy protection is taken more seriously. As
required by the Health Insurance Portability and
Accountability Act (HIPAA), it is necessary to protect the
privacy of patients and ensure the security of the medical
data. To address this problem, released datasets must be
modified unavoidably. We propose a method called Hybrid
approach for privacy preserving and implemented it. First we
randomized the original data. Then we have applied
generalization on randomized or modified data. This
technique protect private data with better accuracy, also it can
reconstruct original data and provide data with no information
loss, makes usability of data.
Survey on Medical Data Sharing Systems with NTRUIRJET Journal
The article discusses international issues. It mentions that globalization has increased economic interdependence between nations while also raising tensions over immigration and trade. Solutions will require cooperation and compromise and a recognition that isolationism is not a viable strategy in an interconnected world.
This is module 2 in the EDI Data Publishing training course. In this module, you will learn about the Environmental Data Initiative, the project that created these trainings. EDI operates the EDI Data Repository and has curators on staff to help scientists deposit their data.
Using Randomized Response Techniques for Privacy-Preserving Data Mining14894
This document proposes using randomized response techniques to conduct privacy-preserving data mining and build decision tree classifiers from disguised data. It presents a method called Multivariate Randomized Response (MRR) that extends randomized response to handle multiple attributes. Experiments show that while the data is disguised, decision trees built from it can still achieve high accuracy compared to trees built from original data, if the randomization parameter is chosen appropriately. The accuracy is affected by this randomization parameter.
The document discusses open data and data sharing, including defining open data, the benefits of open data, overcoming barriers to opening data such as concerns about scooping and sensitive data, best practices for making data open through formats, licensing and description, and the role of research databases and data citation in promoting open data.
Trust threads: Provenance for Data Reuse in Long Tail ScienceBeth Plale
Invited Colloquium talk, Apr 23, 2015, Dept of Information and Library Science, School of Informatics and Computing, Indiana University. Abstract: The world contains a vast amount of digital information which grows vaster ever more rapidly. This makes it possible to do many things on an unprecedented scale: spot social trends, prevent diseases, increase fresh water supplies, accelerate innovation, and so on. As science and technology innovation is essential to improved public health and welfare, the growing sources of data can unlock more secrets. But the rapid growth of data makes accountability and transparency of research increasingly difficult. Data that are not adequately described are not useable except within the research lab that produced it. Data that are intentionally or unintentionally inaccessible or difficult to access and verify are not available to contribute to new forms of research. In this talk I show that data can carry with it thin threads of information that connect it to both its past and its future, forming its lineage particularly as it transitions into a shareable dataset residing in a public repository. In carrying this minimal provenance, the data becomes more trustworthy. This thread of trust is a critical element to the successful sharing, use, and reuse of big data in science and technology research in the future.
This document discusses licensing research data for reuse. It begins by providing a scenario where a user has downloaded a dataset but is unsure what they can do with the data due to licensing. It then discusses that licensing is critical to enabling data reuse and citation. It provides information on AusGOAL, the Australian open access and licensing framework, and notes it is recommended for data publishing by ANDS partners. It also includes links to licensing guides and FAQs. In summary, the document emphasizes the importance of data licensing for enabling reuse and outlines Australia's recommended licensing system.
A Survey Paper on an Integrated Approach for Privacy Preserving In High Dimen...IJSRD
Data mining is a technique which is used for extraction of knowledge and information from large amount of data collected by hospitals, government and individuals. The term data mining is also referred as knowledge mining from databases. The major challenge in data mining is ensuring security and privacy of data in databases, because data sharing is common at organizational level. The data in databases comes from a number of sources like – medical, financial, library, marketing, shopping record etc so it is foremost task for anyone to keep secure that data. The objective is to achieve fully privacy preserved data without affecting the data utility in databases. i.e. how data is used or transferred between organizations so that data integrity remains in database but sensitive and confidential data is preserved. This paper presents a brief study about different PPDM techniques like- Randomization, perturbation, Slicing, summarization etc. by use of which the data privacy can be preserved. The technique for which the best computational and theoretical outcome is achieved is chosen for privacy preserving in high dimensional data.
The document outlines a lecture on privacy preserving data mining. It discusses the motivation for privacy preserving data mining, including the need to analyze sensitive individual data for applications like detecting fraud or disease outbreaks while maintaining privacy. It covers the scope, typical architecture involving modifying original data, common techniques like data perturbation and cryptographic methods, advantages like enabling large data sharing, and applications like securing medical databases. The conclusion emphasizes that privacy preserving data mining has become important for conducting analytics while respecting individuals' privacy rights.
Framework for efficient transformation for complex medical data for improving...IJECEIAES
The adoption of various technological advancement has been already adopted in the area of healthcare sector. This adoption facilitates involuntary generation of medical data that can be autonomously programmed to be forwarded to a destined hub in the form of cloud storage units. However, owing to such technologies there is massive formation of complex medical data that significantly acts as an overhead towards performing analytical operation as well as unwanted storage utilization. Therefore, the proposed system implements a novel transformation technique that is capable of using a template based stucture over cloud for generating structured data from highly unstructured data in a non-conventional manner. The contribution of the propsoed methodology is that it offers faster processing and storage optimization. The study outcome also proves this fact to show propsoed scheme excels better in performance in contrast to existing data transformation scheme.
Pistoia Alliance conference April 2016: Big Data: Eric LittlePistoia Alliance
The document discusses moving from simply analyzing large amounts of data (Big Data) to performing more advanced analysis (Big Analysis) by combining semantic technologies with traditional data science methods. It proposes that Big Analysis allows for a new approach to analysis by using both logic-based semantic reasoning and statistics-based reasoning to provide deeper insights from complex data. The dawn of Big Analysis represents a natural evolution from Big Data to Big Content by integrating different technologies for more informed decision making.
The National Cybersecurity Center of Excellence (NCCoE) at the National Institute of Standards and Technology is inviting feedback on a draft project to address cybersecurity challenges with wireless infusion pumps in hospitals. The project aims to identify security risks posed by connecting medical devices to networks and define solutions to protect the devices from malware or hacking. The NCCoE is collaborating with the Technological Leadership Institute and Minnesota medical providers on a use case that describes the challenge and desired security characteristics. The use case will be finalized and used to develop a practice guide with example solutions to securely deploy wireless infusion pumps.
Impact of big data congestion in IT: An adaptive knowledgebased Bayesian networkIJECEIAES
Recent progress on real-time systems are growing high in information technology which is showing importance in every single innovative field. Different applications in IT simultaneously produce the enormous measure of information that should be taken care of. In this paper, a novel algorithm of adaptive knowledge-based Bayesian network is proposed to deal with the impact of big data congestion in decision processing. A Bayesian system show is utilized to oversee learning arrangement toward all path for the basic leadership process. Information of Bayesian systems is routinely discharged as an ideal arrangement, where the examination work is to find a development that misuses a measurably inspired score. By and large, available information apparatuses manage this ideal arrangement by methods for normal hunt strategies. As it required enormous measure of information space, along these lines it is a tedious method that ought to be stayed away from. The circumstance ends up unequivocal once huge information include in hunting down ideal arrangement. A calculation is acquainted with achieve quicker preparing of ideal arrangement by constraining the pursuit information space. The proposed algorithm consists of recursive calculation intthe inquiry space. The outcome demonstrates that the ideal component of the proposed algorithm can deal with enormous information by processing time, and a higher level of expectation rates.
Cluster Based Access Privilege Management Scheme for DatabasesEditor IJMTER
Knowledge discovery is carried out using the data mining techniques. Association rule mining,
classification and clustering operations are carried out under data mining. Clustering method is used to group up the
records based on the relevancy. Distance or similarity measures are used to estimate the transaction relationship.
Census data and medical data are referred as micro data. Data publish schemes are used to provide private data for
analysis. Privacy preservation is used to protect private data values. Anonymity is considered in the privacy
preservation process.
Data values are allowed to authorized users using the access control models. Privacy Protection Mechanism
(PPM) uses suppression and generalization of relational data to anonymize and satisfy privacy needs. Accuracyconstrained privacy-preserving access control framework is used to manage access control in relational database. The
access control policies define selection predicates available to roles while the privacy requirement is to satisfy the kanonymity or l-diversity. Imprecision bound constraint is assigned for each selection predicate. k-anonymous
Partitioning with Imprecision Bounds (k-PIB) is used to estimate accuracy and privacy constraints. Role-based Access
Control (RBAC) allows defining permissions on objects based on roles in an organization. Top Down Selection
Mondrian (TDSM) algorithm is used for query workload-based anonymization. The Top Down Selection Mondrian
(TDSM) algorithm is constructed using greedy heuristics and kd-tree model. Query cuts are selected with minimum
bounds in Top-Down Heuristic 1 algorithm (TDH1). The query bounds are updated as the partitions are added to the
output in Top-Down Heuristic 2 algorithm (TDH2). The cost of reduced precision in the query results is used in TopDown Heuristic 3 algorithm (TDH3). Repartitioning algorithm is used to reduce the total imprecision for the queries.
The privacy preserved access privilege management scheme is enhanced to provide incremental mining
features. Data insert, delete and update operations are connected with the partition management mechanism. Cell level
access control is provided with differential privacy method. Dynamic role management model is integrated with the
access control policy mechanism for query predicates.
The Role of the FAIR Guiding Principles for an effective Learning Health SystemMichel Dumontier
he learning health system (LHS) is an integrated social and technological system that embeds continuous improvement and innovation for the effective delivery of healthcare. A crucial part of the LHS lies in how the underlying information system will secure and take advantage of relevant knowledge assets towards supporting complex and unusual clinical decision making, facilitating public health surveillance, and aiding comparative effectiveness research. However, key knowledge assets remain difficult to obtain and reuse, particularly in a decentralized context. In this talk, I will discuss the role of the Findable, Accessible, Interoperable, and Reusable (FAIR) Guiding Principles towards the realization of the LHS, along with emerging technologies to publish and refine clinical research and knowledge derived therein.
Keynote given for 2021 Knowledge Representation for Health Care http://banzai-deim.urv.net/events/KR4HC-2021/
The Tryggve project facilitates cross-border biomedical research by providing secure computing services across the Nordic countries. These services allow sensitive human data to be analyzed while protecting individual privacy through secure data storage, transfer and computing environments. The goal is to enable research collaboration while preventing unauthorized access to personal health data.
The document discusses the context and goals of e-science and e-research, including enabling collaboration through distributed computation and data sharing. It provides examples of UK e-science initiatives like national centers and describes the role of the National e-Science Centre in Glasgow in supporting various projects through grid computing resources and expertise. Security challenges around authentication, authorization and auditing are discussed in the context of user-oriented and federated approaches.
This document discusses data safe havens and how they could potentially be incorporated into the European Open Science Cloud (EOSC) to enable research using sensitive data. It describes how data safe havens provide a secure environment for working with medical, social, and other restricted data according to national information governance policies. The document then outlines the Caldicott framework for governing health data research in the UK, as well as specific examples like the Farr Institute and NHS Scotland's approach. It discusses how data linkage projects are currently conducted securely in Scotland's national safe haven. Finally, it raises challenges around harmonizing different countries' information governance policies and ensuring the right support services and standards are in place to enable this kind of research at a European level
Privacy Perserving DataBases, how they are managed, built and secured. with an introduction to main methods of Anonymization techniques, PPDB data mining, P3P and Hippocratic DBs.
Focusing on health verticle consistent with Open Knowledge Networking initiative. Also see a relevant review of: Contextualized Knowledge Graph portal: https://www.slideshare.net/ntkimvinh7/ckg-portal-a-knowledge-publishing-proposal-for-open-knowledge-network
VOLUME-7 ISSUE-8, AUGUST 2019 , International Journal of Research in Advent Technology (IJRAT) , ISSN: 2321-9637 (Online) Published By: MG Aricent Pvt Ltd
Privacy preserving in data mining with hybrid approachNarendra Dhadhal
The document discusses privacy preserving techniques in data mining. It outlines various privacy preserving approaches like randomization, encryption, and anonymization. K-anonymization is described as an important anonymization technique that involves generalization and suppression of data to ensure each record is indistinguishable from at least k-1 other records. The document also reviews several research papers on privacy preserving data mining and discusses issues like homogeneity attacks with k-anonymization. A hybrid approach combining k-anonymization with perturbation is proposed to better protect sensitive data privacy.
Performance Analysis of Hybrid Approach for Privacy Preserving in Data Miningidescitation
Now-a day’s data sharing between two organizations
is common in many application areas like business planning
or marketing. When data are to be shared between parties,
there could be some sensitive data which should not be
disclosed to the other parties. Also medical records are more
sensitive so, privacy protection is taken more seriously. As
required by the Health Insurance Portability and
Accountability Act (HIPAA), it is necessary to protect the
privacy of patients and ensure the security of the medical
data. To address this problem, released datasets must be
modified unavoidably. We propose a method called Hybrid
approach for privacy preserving and implemented it. First we
randomized the original data. Then we have applied
generalization on randomized or modified data. This
technique protect private data with better accuracy, also it can
reconstruct original data and provide data with no information
loss, makes usability of data.
Survey on Medical Data Sharing Systems with NTRUIRJET Journal
The article discusses international issues. It mentions that globalization has increased economic interdependence between nations while also raising tensions over immigration and trade. Solutions will require cooperation and compromise and a recognition that isolationism is not a viable strategy in an interconnected world.
This is module 2 in the EDI Data Publishing training course. In this module, you will learn about the Environmental Data Initiative, the project that created these trainings. EDI operates the EDI Data Repository and has curators on staff to help scientists deposit their data.
Using Randomized Response Techniques for Privacy-Preserving Data Mining14894
This document proposes using randomized response techniques to conduct privacy-preserving data mining and build decision tree classifiers from disguised data. It presents a method called Multivariate Randomized Response (MRR) that extends randomized response to handle multiple attributes. Experiments show that while the data is disguised, decision trees built from it can still achieve high accuracy compared to trees built from original data, if the randomization parameter is chosen appropriately. The accuracy is affected by this randomization parameter.
The document discusses open data and data sharing, including defining open data, the benefits of open data, overcoming barriers to opening data such as concerns about scooping and sensitive data, best practices for making data open through formats, licensing and description, and the role of research databases and data citation in promoting open data.
Trust threads: Provenance for Data Reuse in Long Tail ScienceBeth Plale
Invited Colloquium talk, Apr 23, 2015, Dept of Information and Library Science, School of Informatics and Computing, Indiana University. Abstract: The world contains a vast amount of digital information which grows vaster ever more rapidly. This makes it possible to do many things on an unprecedented scale: spot social trends, prevent diseases, increase fresh water supplies, accelerate innovation, and so on. As science and technology innovation is essential to improved public health and welfare, the growing sources of data can unlock more secrets. But the rapid growth of data makes accountability and transparency of research increasingly difficult. Data that are not adequately described are not useable except within the research lab that produced it. Data that are intentionally or unintentionally inaccessible or difficult to access and verify are not available to contribute to new forms of research. In this talk I show that data can carry with it thin threads of information that connect it to both its past and its future, forming its lineage particularly as it transitions into a shareable dataset residing in a public repository. In carrying this minimal provenance, the data becomes more trustworthy. This thread of trust is a critical element to the successful sharing, use, and reuse of big data in science and technology research in the future.
This document discusses licensing research data for reuse. It begins by providing a scenario where a user has downloaded a dataset but is unsure what they can do with the data due to licensing. It then discusses that licensing is critical to enabling data reuse and citation. It provides information on AusGOAL, the Australian open access and licensing framework, and notes it is recommended for data publishing by ANDS partners. It also includes links to licensing guides and FAQs. In summary, the document emphasizes the importance of data licensing for enabling reuse and outlines Australia's recommended licensing system.
A Survey Paper on an Integrated Approach for Privacy Preserving In High Dimen...IJSRD
Data mining is a technique which is used for extraction of knowledge and information from large amount of data collected by hospitals, government and individuals. The term data mining is also referred as knowledge mining from databases. The major challenge in data mining is ensuring security and privacy of data in databases, because data sharing is common at organizational level. The data in databases comes from a number of sources like – medical, financial, library, marketing, shopping record etc so it is foremost task for anyone to keep secure that data. The objective is to achieve fully privacy preserved data without affecting the data utility in databases. i.e. how data is used or transferred between organizations so that data integrity remains in database but sensitive and confidential data is preserved. This paper presents a brief study about different PPDM techniques like- Randomization, perturbation, Slicing, summarization etc. by use of which the data privacy can be preserved. The technique for which the best computational and theoretical outcome is achieved is chosen for privacy preserving in high dimensional data.
The document outlines a lecture on privacy preserving data mining. It discusses the motivation for privacy preserving data mining, including the need to analyze sensitive individual data for applications like detecting fraud or disease outbreaks while maintaining privacy. It covers the scope, typical architecture involving modifying original data, common techniques like data perturbation and cryptographic methods, advantages like enabling large data sharing, and applications like securing medical databases. The conclusion emphasizes that privacy preserving data mining has become important for conducting analytics while respecting individuals' privacy rights.
Framework for efficient transformation for complex medical data for improving...IJECEIAES
The adoption of various technological advancement has been already adopted in the area of healthcare sector. This adoption facilitates involuntary generation of medical data that can be autonomously programmed to be forwarded to a destined hub in the form of cloud storage units. However, owing to such technologies there is massive formation of complex medical data that significantly acts as an overhead towards performing analytical operation as well as unwanted storage utilization. Therefore, the proposed system implements a novel transformation technique that is capable of using a template based stucture over cloud for generating structured data from highly unstructured data in a non-conventional manner. The contribution of the propsoed methodology is that it offers faster processing and storage optimization. The study outcome also proves this fact to show propsoed scheme excels better in performance in contrast to existing data transformation scheme.
Pistoia Alliance conference April 2016: Big Data: Eric LittlePistoia Alliance
The document discusses moving from simply analyzing large amounts of data (Big Data) to performing more advanced analysis (Big Analysis) by combining semantic technologies with traditional data science methods. It proposes that Big Analysis allows for a new approach to analysis by using both logic-based semantic reasoning and statistics-based reasoning to provide deeper insights from complex data. The dawn of Big Analysis represents a natural evolution from Big Data to Big Content by integrating different technologies for more informed decision making.
The National Cybersecurity Center of Excellence (NCCoE) at the National Institute of Standards and Technology is inviting feedback on a draft project to address cybersecurity challenges with wireless infusion pumps in hospitals. The project aims to identify security risks posed by connecting medical devices to networks and define solutions to protect the devices from malware or hacking. The NCCoE is collaborating with the Technological Leadership Institute and Minnesota medical providers on a use case that describes the challenge and desired security characteristics. The use case will be finalized and used to develop a practice guide with example solutions to securely deploy wireless infusion pumps.
Impact of big data congestion in IT: An adaptive knowledgebased Bayesian networkIJECEIAES
Recent progress on real-time systems are growing high in information technology which is showing importance in every single innovative field. Different applications in IT simultaneously produce the enormous measure of information that should be taken care of. In this paper, a novel algorithm of adaptive knowledge-based Bayesian network is proposed to deal with the impact of big data congestion in decision processing. A Bayesian system show is utilized to oversee learning arrangement toward all path for the basic leadership process. Information of Bayesian systems is routinely discharged as an ideal arrangement, where the examination work is to find a development that misuses a measurably inspired score. By and large, available information apparatuses manage this ideal arrangement by methods for normal hunt strategies. As it required enormous measure of information space, along these lines it is a tedious method that ought to be stayed away from. The circumstance ends up unequivocal once huge information include in hunting down ideal arrangement. A calculation is acquainted with achieve quicker preparing of ideal arrangement by constraining the pursuit information space. The proposed algorithm consists of recursive calculation intthe inquiry space. The outcome demonstrates that the ideal component of the proposed algorithm can deal with enormous information by processing time, and a higher level of expectation rates.
Cluster Based Access Privilege Management Scheme for DatabasesEditor IJMTER
Knowledge discovery is carried out using the data mining techniques. Association rule mining,
classification and clustering operations are carried out under data mining. Clustering method is used to group up the
records based on the relevancy. Distance or similarity measures are used to estimate the transaction relationship.
Census data and medical data are referred as micro data. Data publish schemes are used to provide private data for
analysis. Privacy preservation is used to protect private data values. Anonymity is considered in the privacy
preservation process.
Data values are allowed to authorized users using the access control models. Privacy Protection Mechanism
(PPM) uses suppression and generalization of relational data to anonymize and satisfy privacy needs. Accuracyconstrained privacy-preserving access control framework is used to manage access control in relational database. The
access control policies define selection predicates available to roles while the privacy requirement is to satisfy the kanonymity or l-diversity. Imprecision bound constraint is assigned for each selection predicate. k-anonymous
Partitioning with Imprecision Bounds (k-PIB) is used to estimate accuracy and privacy constraints. Role-based Access
Control (RBAC) allows defining permissions on objects based on roles in an organization. Top Down Selection
Mondrian (TDSM) algorithm is used for query workload-based anonymization. The Top Down Selection Mondrian
(TDSM) algorithm is constructed using greedy heuristics and kd-tree model. Query cuts are selected with minimum
bounds in Top-Down Heuristic 1 algorithm (TDH1). The query bounds are updated as the partitions are added to the
output in Top-Down Heuristic 2 algorithm (TDH2). The cost of reduced precision in the query results is used in TopDown Heuristic 3 algorithm (TDH3). Repartitioning algorithm is used to reduce the total imprecision for the queries.
The privacy preserved access privilege management scheme is enhanced to provide incremental mining
features. Data insert, delete and update operations are connected with the partition management mechanism. Cell level
access control is provided with differential privacy method. Dynamic role management model is integrated with the
access control policy mechanism for query predicates.
The Role of the FAIR Guiding Principles for an effective Learning Health SystemMichel Dumontier
he learning health system (LHS) is an integrated social and technological system that embeds continuous improvement and innovation for the effective delivery of healthcare. A crucial part of the LHS lies in how the underlying information system will secure and take advantage of relevant knowledge assets towards supporting complex and unusual clinical decision making, facilitating public health surveillance, and aiding comparative effectiveness research. However, key knowledge assets remain difficult to obtain and reuse, particularly in a decentralized context. In this talk, I will discuss the role of the Findable, Accessible, Interoperable, and Reusable (FAIR) Guiding Principles towards the realization of the LHS, along with emerging technologies to publish and refine clinical research and knowledge derived therein.
Keynote given for 2021 Knowledge Representation for Health Care http://banzai-deim.urv.net/events/KR4HC-2021/
The Tryggve project facilitates cross-border biomedical research by providing secure computing services across the Nordic countries. These services allow sensitive human data to be analyzed while protecting individual privacy through secure data storage, transfer and computing environments. The goal is to enable research collaboration while preventing unauthorized access to personal health data.
The document discusses the context and goals of e-science and e-research, including enabling collaboration through distributed computation and data sharing. It provides examples of UK e-science initiatives like national centers and describes the role of the National e-Science Centre in Glasgow in supporting various projects through grid computing resources and expertise. Security challenges around authentication, authorization and auditing are discussed in the context of user-oriented and federated approaches.
This document discusses data safe havens and how they could potentially be incorporated into the European Open Science Cloud (EOSC) to enable research using sensitive data. It describes how data safe havens provide a secure environment for working with medical, social, and other restricted data according to national information governance policies. The document then outlines the Caldicott framework for governing health data research in the UK, as well as specific examples like the Farr Institute and NHS Scotland's approach. It discusses how data linkage projects are currently conducted securely in Scotland's national safe haven. Finally, it raises challenges around harmonizing different countries' information governance policies and ensuring the right support services and standards are in place to enable this kind of research at a European level
Security Issues in Biomedical Wireless Sensor Networks Applications: A SurveyIJARTES
Abstract The use of wireless sensor networks in healthcare
applications is growing in a fast pace. Numerous applications
such as heart rate monitor, blood pressure monitor and
endoscopic capsule are already in use. To address the growing
use of sensor technology in this area, a new field known as
wireless body area networks has emerged. As most devices
and their applications are wireless in nature, security and
privacy concerns are among major areas of concern. Body
area networks can collect information about an individual’s
health, fitness and energy expenditure. Comprising body
sensors that communicate wirelessly with the patients
control device for monitoring and external communication.
This paper provides the challenges of using the wireless
sensor network in biomedical field and how to solve most of
these issues. To analyze the different security strategies in
Wireless Sensor Networks and propose this system to give
highest quality medical care with full security in their
reliability
This is particularly the case on e Health monitoring applications for chronic patients, Where Patients
monitoring refers to a continuous observation of patient’s condition (physiological and physical) traditionally
performed by one or several body sensors. The architecture for this system is based on medical sensors which
measure patients’ physical parameters by using wireless sensor networks (WSNs). These sensors transfer data
from patients’ bodies over the wireless network to the cloud environment. The system is aimed to prevent delays
in the arrival of patients’ medical information to the healthcare providers, Therefore, patients will have a high
quality services because the e heath smart system supports medical staff by providing real-time data gathering,
eliminating manual data collection, enabling the monitoring of huge numbers of patients. We underline the
necessity of the analysis of data quality on e-Health applications, especially concerning remote monitoring and
assistance of patients with chronic diseases.
A Data-centric perspective on Data-driven healthcare: a short overviewPaolo Missier
a brief intro on the data challenges associated with working with Health Care data, with a few examples, both from literature and our own, of traditional approaches (Latent Class Analysis, Topic Modelling) and a perspective on Language-based modelling for Electronic Health Records (EHR).
probably more references than actual content in here!
HPC and Precision Medicine: A New Framework for Alzheimer's and Parkinson'sinside-BigData.com
In this deck from the HPC User Forum in Tucson, Joe Lombardo from UNLV presents: HPC and Precision Medicine - A New Framework for Alzheimer's and Parkinson's.
"The University of Nevada, Las Vegas and the Cleveland Clinic Lou Ruvo Center for Brain Health have been awarded an $11 million federal grant from the National Institutes of Health and National Institute of General Medical Sciences to advance the understanding of Alzheimer's and Parkinson's diseases. In this session, we will present how UNLV's National Supercomputing Institute plays a critical role in this research by fusing brain imaging, neuropsychological and behavioral studies along with the diagnostic exome sequencing models to increase our knowledge of dementia-related and age-associated degenerative disorders."
Watch the video: https://wp.me/p3RLHQ-iws
Learn more: https://www.unlv.edu/news/release/unlv-receives-nih-grant-alzheimers-disease-research
and
http://hpcuserforum.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
ANDS health and medical data webinar 16 May. Storing and Publishing Health an...ARDC
Dr Jeff Christiansen (QCIF) introduced med.data.edu.au, a national facility to provide petabyte-scale research data storage, and related high-speed networked computational services, to Australian medical and health research organisations.
Webinar: https://www.youtube.com/watch?v=5jwBwDJrWAs
Jeff Christiansen Snippet: https://www.youtube.com/watch?v=PV_vuUKRm6w
Transcript: https://www.slideshare.net/AustralianNationalDataService/transcript-storing-and-publishing-health-and-medical-data-16052017
The document proposes a system for securely distributing and analyzing patient data from wireless medical sensor networks. It discusses distributing patient data across multiple database servers and using Paillier cryptography to perform statistical analysis without compromising privacy. The key contributions are preventing inside attacks by distributed storage and allowing data retrieval if a server is compromised through a re-encryption technique. The goals are to secure data transmission and storage as well as stop insiders from revealing private patient information.
IRJET-A Survey on provide security to wireless medical sensor dataIRJET Journal
This document discusses providing security for wireless medical sensor data. It first reviews related work on securing wireless medical sensor networks using cryptosystems like Paillier and ElGamal. It then proposes a system that uses these cryptosystems to encrypt and distribute patient data across multiple data servers. This would preserve patient privacy as long as no single server is compromised. The system aims to allow medical analysis of distributed encrypted data without revealing individual patient information.
A Survey on provide security to wireless medical sensor dataIRJET Journal
This document summarizes a survey on providing security for wireless medical sensor data. The survey examines existing approaches that use cryptosystems like Paillier and ElGamal to securely distribute patient data across multiple data servers. This prevents privacy compromises if a single data server is breached. The proposed system would use these cryptosystems to encrypt patient data captured by wireless medical sensors and split the encrypted data across several data servers. This would allow analysis of patient data without compromising privacy as long as not all servers are compromised.
Private & Secure Data Tx Presentation I (1).pptxKomal526846
This document proposes a system for securely transmitting private medical data using QR codes and cloud computing. It involves developing an Android and web application that allows users to register and login to store their personal medical records and generate a QR code. When scanned, the QR code would provide access to the encrypted medical records stored in the cloud. The goals are to use QR codes to store medical records privately, reduce time in hospitals by accessing records quickly, and encrypt records for security using cryptographic techniques like AES and MD5. The system architecture and hardware/software requirements are also outlined.
Case Study 4by Anil NayakiSubmission dat e 12- Dec- 20.docxwendolynhalbert
Case Study 4
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CASE STUDY 9
ST. LUKE'S HEALTH CARE SYSTEM
Hospitals have been some of the earliest adopters of wireless local area
networks (WLANs). The clinician user population is typically mobile and
spread out across a number of buildings, with a need to enter and access
data in real time. St. Luke's Episcopal Health System in Houston, Texas
(www.stlukestexas.com) is a good example of a hospital that has made
effective use wireless technologies to streamline clinical work processes.
Their wireless network is distributed throughout several hospital buildings
and is used in many different applications. The majority of the St. Luke’s
staff uses wireless devices to access data in real-time, 24 hours a day.
Examples include the following:
• Diagnosing patients and charting their progress: Doctors and
nurses use wireless laptops and tablet PCs to track and chart patient
care data.
• Prescriptions: Medications are dispensed from a cart that is wheeled
from room to room. Clinician uses a wireless scanner to scan the
patient's ID bracelet. ...
The document discusses issues biomedical projects face when accessing clinical datasets due to disparate data formats. It presents a proposed solution of annotating clinical datasets with openEHR Archetypes, which are standards-based models of clinical concepts, to enable computer-based discovery of clinical information. The proposed technique involves transforming Archetypes into an "ontology of reality" by identifying clinical concepts and terminology codes to annotate datasets. This would allow complete clinical concepts, rather than just attributes, to be annotated and discovered from datasets.
Data Harmonization for a Molecularly Driven Health SystemWarren Kibbe
Seminar for Dr. Min Zhang's Purdue Bioinformatics Seminar Series. Touched on learning health systems, the Gen3 Data Commons, the NCI Genomic Data Commons, Data Harmonization, FAIR, and open science.
The Future: Overcoming the Barriers to Using NHS Clinical Data For Research P...Mark Hawker
The document summarizes the barriers to using clinical data from the UK National Health Service (NHS) for research purposes and potential solutions. It discusses issues with data quality, coding, and linking records across disconnected systems. However, integrated electronic health records could enable large cohort studies and clinical trials if privacy and security are ensured. The author proposes training for clinical and research staff on database design, standards, and information sharing to help align records and support strategic health research using NHS data.
Data Harmonization for a Molecularly Driven Health SystemWarren Kibbe
Maximizing the value of data, computing, data science in an academic medical center, or 'towards a molecularly informed Learning Health System. Given in October at the University of Florida in Gainesville
Patient Privacy Control for Health Care in Cloud Computing SystemIRJET Journal
This document describes a cloud-based healthcare system that aims to provide secure sharing of patient health information between healthcare providers while maintaining patient privacy. The system uses authentication and access control schemes along with encryption techniques like attribute-based encryption to restrict access to patient data based on user attributes. It presents the design of the system architecture, which includes patient, doctor and lab units. It also outlines the main algorithms used in the system for key generation, signing data, verifying signatures, and simulating transcript data to protect patient identities. The goal of the system is to enable secure telemedicine and remote diagnosis capabilities while ensuring patient privacy in distributed cloud healthcare computing.
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
Data mining techniques are used for a variety of applications. In healthcare industry, datamining plays an important
role in predicting diseases. For detecting a disease number of tests should be required from the patient. But using data
mining technique the number of tests can be reduced. This reduced test plays an important role in time and performance.
This report analyses data mining techniques which can be used for predicting different types of diseases. This report reviewed
the research papers which mainly concentrate on predicting various disease
A Secure and Efficient Cloud centric Internet of Medical Things-Enabled Smart...suherashaik2003
This document outlines a student project proposal for developing a secure and efficient cloud-based medical data sharing system using public verifiability. The proposed system would use an escrow-free identity-based aggregate signcryption scheme to securely transmit medical data from sensors on a patient's body to a medical cloud server via a smartphone. This would provide security features like anonymity, integrity of stored data, and authentication. The project details technologies like Java, hardware requirements, and references several related works in cloud computing and IoT for healthcare.
Similar to BigInsight seminar on Practical Privacy-Preserving Distributed Statistical Computation (20)
Den europeiske studentundersøkelsen er en internasjonal spørreundersøkelsen rettet mot studenter i høyere utdanning. Undersøkelsen gjennomføres hvert 3. år. Nettside: www.eurostudent.eu. 23. august presenterte SSB tallene på et frokostseminar.
På seminaret pratet forskerne fra SSB om:
Når passerer vi 6 millioner?
Vil fruktbarheten fortsette å synke?
Hvilke kommuner får størst vekst og nedgang i befolkningen?
Når og hvor treffer «eldrebølgen»?
Hvor mange innvandrere bor i Norge i 2060?
BigInsight seminar on Practical Privacy-Preserving Distributed Statistical Co...Statistisk sentralbyrå
Seminar Monday March 5th 2018 by BigInsight and Statistics Norway: Valuable knowledge can be obtained by combining data from two or more sources, but exchanging and linking data are often unacceptable due to confidentiality and privacy concerns. Consequently, important discoveries that are important to society could be hampered. Therefore, data and results need to be processed in a way that preserves privacy. New approaches and computing methods for analyzing data distributed across multiple data sources while protecting privacy are being developed. This seminar addresses these issues with several speakers connected to Norwegian Centre for E-health Research in Tromsø. The director of the Cancer Registry of Norway is also among the speakers.
BigInsight seminar on Practical Privacy-Preserving Distributed Statistical Co...Statistisk sentralbyrå
Norwegian health registries collect data from 17 central and 54 clinical registries for purposes like disease assessment and prevention. There are concerns about safely linking these datasets while avoiding reidentification. An example showed one woman could be reidentified from her birth month, cervical exam dates and cancer diagnosis. To reduce this risk, dates were altered by removing days, changing months randomly by -4 to +4 months, and removing birth months. This "fuzzification" technique significantly reduced the reidentification risk according to tools like ARX. However, current national data platforms still rely heavily on trust rather than technical solutions, which is insufficient for large linked datasets. Better anonymization protocols are needed to balance open analysis and individual privacy.
BigInsight seminar on Practical Privacy-Preserving Distributed Statistical Co...Statistisk sentralbyrå
Seminar Monday March 5th 2018 by BigInsight and Statistics Norway: Presentation by Stein Olav Skrøvseth: The national role of the Norwegian Centre for E-health Research and its focus on Health data analytics
BigInsight seminar on Practical Privacy-Preserving Distributed Statistical Co...Statistisk sentralbyrå
Seminar Monday March 5th 2018 by BigInsight and Statistics Norway: Presentation by Kassaye Yitbarek Yigzaw. Privacy-preserving collection and analyses of citizen-generated data.
BigInsight seminar on Practical Privacy-Preserving Distributed Statistical Co...Statistisk sentralbyrå
Seminar Monday March 5th 2018 by BigInsight and Statistics Norway: Presentation by Kassaye Yitbarek Yigzaw. Distributed data analysis in the face og privacy concerns.
BigInsight seminar on Practical Privacy-Preserving Distributed Statistical Co...Statistisk sentralbyrå
Seminar Monday March 5th 2018 by BigInsight and Statistics Norway: Presentation by Øyvind Langsrud and Johan Heldal. Modernization, big data and confidentiality at Statistics Norway.
Innvandrere i Norge 2017, presentasjon fra frokostseminar 11.12.2017Statistisk sentralbyrå
Gjennom 2017 har SSB publisert en rekke analyseartikler som gir en overordnet beskrivelse av innvandrere og deres norskfødte barn i Norge. 11. desember ble noen av disse analysene presentert på frokostseminar hos SSB, og dette er presentasjonen som ble brukt.
Presentasjon fra frokostseminar om kulturbruk og - vaner. Norsk kulturbarometer med tall fra 1990-tallet og frem til i dag. Oversikt over hvem som bruker ulike kulturtilbud som museum, konserter, kino, teater m.m.
Frokostseminar 24. mai 2017 hvor rapporten "Levekår blant innvandrere i Norge 2016" ble presentert. Rapporten er basert på intervjuer med innvandrere og sier mye om hvordan innvandrere har det i Norge i dag. Mer om rapporten her: http://www.ssb.no/innvandring-og-innvandrere/artikler-og-publikasjoner/innvandreres-velferd-og-levekar-slik-har-innvandrerne-i-norge-det
SSB: Fagseminar om innvandring og inntektsutvikling 16. mars 2017 Statistisk sentralbyrå
Inntekt er sammen med deltakelse i arbeidslivet og utdanning, de viktigste kriteriene for å måle integrering. SSB har tall på dette langt tilbake i tid, slik at vi kan følge utviklingen.
Program:
• Innvandreres inntekter, Jon Epland, Seksjon for inntekts- og lønnsstatistikk
• Etter trygdemottak – hva skjer da? Tor Morten Normann, Seksjon for levekårsstatistikk
• Selvforsørging blant ikke-nordiske innvandrere, Tom Kornstad, Forskningsavdelingen
• Betydningen av innvandring for offentlige finanser i tiårene fremover, Erling Holmøy, Forskningsavdelingen og medlem av Brochmann 2-utvalget
Den 14. desember 2016 ble tidsskriftet Samfunnsspeilet nr. 4 2016 publisert, et temanummer om flyktninger i Norge. I den anledning arrangerte Statistisk sentralbyrå et seminar, hvor fire foredragsholdere presenterte deler av innholdet i Samfunnsspeilets temanummer om flyktninger.
Først fortalte Elisabeth Nørgaard om flyktninger i Norge i dag, deretter presenterte Helge Næsheim status for ulike flyktninggruppers deltakelse i arbeidsmarkedet. Etter det fortalte Minja Tea Dzamarija om familiegjenforening, før seminaret ble avsluttet med et innlegg av Øivin Kleven, som fortalte om flyktningers deltakelse i lokalpolitikken.
Denne presentasjonen er altså en samling av alle disse fire temaene som ble presentert på seminaret i desember 2016.
Statistisk sentralbyrå sitt API mot Statistikkbanken. Presentasjon på Difi sitt Datadelingsforum 31. august 2016. Streamet Video ligger på http://kartverket.23video.com/ssbs-api-mot-statistikkbanken
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Introduction to Jio Cinema**:
- Brief overview of Jio Cinema as a streaming platform.
- Its significance in the Indian market.
- Introduction to retention and engagement strategies in the streaming industry.
2. **Understanding Retention and Engagement**:
- Define retention and engagement in the context of streaming platforms.
- Importance of retaining users in a competitive market.
- Key metrics used to measure retention and engagement.
3. **Jio Cinema's Content Strategy**:
- Analysis of the content library offered by Jio Cinema.
- Focus on exclusive content, originals, and partnerships.
- Catering to diverse audience preferences (regional, genre-specific, etc.).
- User-generated content and interactive features.
4. **Personalization and Recommendation Algorithms**:
- How Jio Cinema leverages user data for personalized recommendations.
- Algorithmic strategies for suggesting content based on user preferences, viewing history, and behavior.
- Dynamic content curation to keep users engaged.
5. **User Experience and Interface Design**:
- Evaluation of Jio Cinema's user interface (UI) and user experience (UX).
- Accessibility features and device compatibility.
- Seamless navigation and search functionality.
- Integration with other Jio services.
6. **Community Building and Social Features**:
- Strategies for fostering a sense of community among users.
- User reviews, ratings, and comments.
- Social sharing and engagement features.
- Interactive events and campaigns.
7. **Retention through Loyalty Programs and Incentives**:
- Overview of loyalty programs and rewards offered by Jio Cinema.
- Subscription plans and benefits.
- Promotional offers, discounts, and partnerships.
- Gamification elements to encourage continued usage.
8. **Customer Support and Feedback Mechanisms**:
- Analysis of Jio Cinema's customer support infrastructure.
- Channels for user feedback and suggestions.
- Handling of user complaints and queries.
- Continuous improvement based on user feedback.
9. **Multichannel Engagement Strategies**:
- Utilization of multiple channels for user engagement (email, push notifications, SMS, etc.).
- Targeted marketing campaigns and promotions.
- Cross-promotion with other Jio services and partnerships.
- Integration with social media platforms.
10. **Data Analytics and Iterative Improvement**:
- Role of data analytics in understanding user behavior and preferences.
- A/B testing and experimentation to optimize engagement strategies.
- Iterative improvement based on data-driven insights.
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
BigInsight seminar on Practical Privacy-Preserving Distributed Statistical Computation
1. The Norwegian Primary Care Research
Network IT infrastructure: The Snow system
Johan Gustav Bellika
Professor Nasjonalt Senter for e-helseforskning
Professor II Institutt for Klinisk Medisin, Helsevitenskapelig fakultet, UiT
Seminar on Practical Privacy-Preserving Distributed Statistical Computations
2018.03.05
Dr. John Snow
(1813 – 1858)
2. Source: WMA Declaration of Helsinki. URL: https://www.wma.net/policies-post/wma-declaration-of-helsinki-ethical-principles-for-medical-research-involving-human-subjects/
http://chrisricecooper.blogspot.no/2015/02/photoessay-on-year-of-ram-by-asian.html
Declaration of Helsinki, Article 6
The primary purpose of medical research involving human
subjects is to understand the causes, development and effects of
diseases and improve preventive, diagnostic and therapeutic
interventions (methods, procedures and treatments).
Even the best proven interventions must be evaluated
continually through research for their safety, effectiveness,
efficiency, accessibility and quality.
3. Declaration of Helsinki, Article 9
It is the duty of physicians who are involved in medical
research to protect the life, health, dignity, integrity, right to
self-determination, privacy, and confidentiality of personal
information of research subjects.
Source: WMA Declaration of Helsinki. URL: https://www.wma.net/policies-post/wma-declaration-of-helsinki-ethical-principles-for-medical-research-involving-human-subjects/
Medical research should be privacy preserving!
4. Objectives for the research infrastructure
• Make participation in research projects easier and more efficient for the GPs
• Reuse health data in a safe and privacy preserving manner
• Complete research projects according to scheduled time and resources
consumption
• Recruit 90-110 GP practices
• Cover 7,5 % of the Norwegian population
6. What is Snow?
• A distributed system
• Enables collection and reuse of anonymous medical data
• Builds and maintains a national online epidemiology-model
• Use the epidemiology model to provide automated IT based health services
• Enable privacy preserving distributed computations on EHR data
• Directed at research, quality improvements, audit, disease surveillance,…
Source:http://upload.wikimedia.org/wikipedia/commons/f/f6/Vibrio_cholerae.jpg
7. Snow architecture
- enables coordinated computations on distributed resources
- a “collaborative Edge computing” infrastructure [1]
S Coord
S
S
S
S
ClientCoord=Snow Coordination server
S= Snow Server in local health institution
Source: [1] Shi W, Cao J, Zhang Q, Li Y, Xu L. Edge Computing: Vision and Challenges. IEEE Internet Things J. oktober 2016;3(5):637–46.
8. Edge computing
“Edge computing refers to the enabling technologies allowing computation to be
performed at the edge of the network”[1].
Beneficial when data is:
• To sensitive (health data)
• To big (genetic data)
• To competitive (data will expose profile of owner)
• +++
Source: [1] Shi W, Cao J, Zhang Q, Li Y, Xu L. Edge Computing: Vision and Challenges. IEEE Internet Things J. oktober 2016;3(5):637–46.
9. The computing entities
• The individual computing process – an “agent”
• One instantiation at each participating Snow server
• One unique communication address for each agent:
agent-user@snow-server-domain/mission_id
• Agents communicates among each other using XMPP messages
• Coordinated computations: “Missions” of multiple agents:
• One “main” coordinating agent
• Multiple computation agents performing computations in parallel
10. Agent distribution scheme
(Collaborative computations at the edges)
Snow coordinator
Main
agent
Snow server Snow server Snow server
Health network
Comput.
agent
Comput.
agent
Comput.
agent
Health institution Health institutionHealth institution
11. • A small computer that fits everywhere
• Snow server software is pre-installed
• Very easy installation
• Remote system administration by the Snow
team at UiT / NSE
• Remove the risk of affecting the stability or
performance of operation critical IT systems
– the electronic health record system
• All data in the box is pseudonymised, both
patient and GPs
• Agents compute on the box
Snow appliance box:
The nodes of the
network
11
12. Data flow in PCRN
Internet Secure health net
GP office 1
Snow
GP
server
EMR
GP office 2
Snow
GP
server
EMR
GP office 3
Snow
GP
server
EMR
Aggregated
data/statistics
Snow
coordinator
server
PCRN net portal
• Distributed data analysis
• Establish projects, invite GPs,
initiate data extraction etc
PCRN internal data
• Epidemiological analyses
• GP and patient data
• Consultation statistics
PCRN CN
Safe haven for data
Research data set
(individual patient data)Secure data storage for
research data set and
advanced data analyses
Local net
= data storage
14. Virtualdataset
Creating a virtual dataset with Emnet/Snow
Researcher/PCRN staff Coordinator
Def Def
Def
Def
Clinical
practice 1
Clinical
practice 2
Clinical
practice 3
Aimed at:
1. Make participation in research projects easier and more efficient for the GPs
2. Support researchers in inclusion of sufficient number of patients in clinical research
3. Support article 9 in Helsinki declaration: Privacy preservation
16. Virtualdataset
Distributed statistical computations with Emnet/Snow
Clinical
practice 1
Clinical
practice 2
Clinical
practice 3
Researcher/PCRN staff Coordinator
Query Query
Query
Query
Result
Secure multi-party computation
(SMC)
Aimed at:
1. Support researchers in inclusion of sufficient number of patients in clinical research
18. Benefits
• Centralised resources as PCRN staff/researchers can help GPs become more
efficient in research.
• Knowledge about the patient populations can be generated directly from the
distributed sources, spanning administrative borders as municipalities, regions,
countries and continents
• Aggregated (non sensitive) statistics can be produced automatically directly from
the sources.
19. Drawbacks
• Two other comparable approaches exists, no standard established
• How to validate correctness of computed statistics is an open research question