This document presents PDDP, a practical system for performing statistical queries over distributed private user data while preserving user privacy. PDDP uses differential privacy to add noise to query results. It limits the ability of malicious users to distort results by splitting answers into binary buckets. The proxy adds blind noise in the form of encrypted coins contributed by clients to achieve differential privacy. PDDP scales to millions of users and tolerates client churn while providing strong privacy guarantees and bounding result distortion.
A Survey of Source Authentication Schemes for Multicast transfer in Adhoc Net...ijsrd.com
An adhoc network is a collection of autonomous nodes with dynamically changing infrastructure. Multicast is a good mechanism for group communication. It can be used in the group oriented applications like video/audio conference, interactive group games, video on demand etc. The security problems obstruct the large deployment of the multicast communication model. Multicast data origin authentication is the main component in the security architecture. The authentication schemes should scalable and efficient against packet loss. In this article we discuss varies authentication scheme for multicast data origin with their advantage and disadvantage
LSTM deep learning method for network intrusion detection system IJECEIAES
The security of the network has become a primary concern for organizations. Attackers use different means to disrupt services, these various attacks push to think of a new way to block them all in one manner. In addition, these intrusions can change and penetrate the devices of security. To solve these issues, we suggest, in this paper, a new idea for Network Intrusion Detection System (NIDS) based on Long Short-Term Memory (LSTM) to recognize menaces and to obtain a long-term memory on them, in order to stop the new attacks that are like the existing ones, and at the same time, to have a single mean to block intrusions. According to the results of the experiments of detections that we have realized, the Accuracy reaches up to 99.98 % and 99.93 % for respectively the classification of two classes and several classes, also the False Positive Rate (FPR) reaches up to only 0,068 % and 0,023 % for respectively the classification of two classes and several classes, which proves that the proposed model is effective, it has a great ability to memorize and differentiate between normal traffic and attacks, and its identification is more accurate than other Machine Learning classifiers.
امروزه با پیشرفت فناوریهای ارتباطی، خصوصاً شبکههای کامپیوتری و اینترنت، تعاملات و فعالیتها در محیطهای مجازی فزونی یافته است. در تعاملات فیزیکی، اعتماد نقش مهمی را در شرایط نایقینی بازی میکند. در فضاهای مجازی تعاملی نیز هنگام تصمیمگیری در مورد ارتباطات، و انتخاب از میان محتواهای ارائه شده میتوان با ارائه تعریف مناسبی از اعتماد و به کارگیری آن، سیستمهای کاراتر و پویاتری طراحی کرد.
در این ارائه به بررسی نحوه تعریف و محاسبه اعتماد در حوزههای کاربردی مختلف میپردازیم. سپس با برخی کاربردهای این مباحث در ایجاد سامانههای کارا و پویا (نظیر سیستمهای توصیه، جمعآوری اخبار، فیلترکردن ایمیل و مسیریابی همتا به همتا) آشنا میشویم.
A Survey of Source Authentication Schemes for Multicast transfer in Adhoc Net...ijsrd.com
An adhoc network is a collection of autonomous nodes with dynamically changing infrastructure. Multicast is a good mechanism for group communication. It can be used in the group oriented applications like video/audio conference, interactive group games, video on demand etc. The security problems obstruct the large deployment of the multicast communication model. Multicast data origin authentication is the main component in the security architecture. The authentication schemes should scalable and efficient against packet loss. In this article we discuss varies authentication scheme for multicast data origin with their advantage and disadvantage
LSTM deep learning method for network intrusion detection system IJECEIAES
The security of the network has become a primary concern for organizations. Attackers use different means to disrupt services, these various attacks push to think of a new way to block them all in one manner. In addition, these intrusions can change and penetrate the devices of security. To solve these issues, we suggest, in this paper, a new idea for Network Intrusion Detection System (NIDS) based on Long Short-Term Memory (LSTM) to recognize menaces and to obtain a long-term memory on them, in order to stop the new attacks that are like the existing ones, and at the same time, to have a single mean to block intrusions. According to the results of the experiments of detections that we have realized, the Accuracy reaches up to 99.98 % and 99.93 % for respectively the classification of two classes and several classes, also the False Positive Rate (FPR) reaches up to only 0,068 % and 0,023 % for respectively the classification of two classes and several classes, which proves that the proposed model is effective, it has a great ability to memorize and differentiate between normal traffic and attacks, and its identification is more accurate than other Machine Learning classifiers.
امروزه با پیشرفت فناوریهای ارتباطی، خصوصاً شبکههای کامپیوتری و اینترنت، تعاملات و فعالیتها در محیطهای مجازی فزونی یافته است. در تعاملات فیزیکی، اعتماد نقش مهمی را در شرایط نایقینی بازی میکند. در فضاهای مجازی تعاملی نیز هنگام تصمیمگیری در مورد ارتباطات، و انتخاب از میان محتواهای ارائه شده میتوان با ارائه تعریف مناسبی از اعتماد و به کارگیری آن، سیستمهای کاراتر و پویاتری طراحی کرد.
در این ارائه به بررسی نحوه تعریف و محاسبه اعتماد در حوزههای کاربردی مختلف میپردازیم. سپس با برخی کاربردهای این مباحث در ایجاد سامانههای کارا و پویا (نظیر سیستمهای توصیه، جمعآوری اخبار، فیلترکردن ایمیل و مسیریابی همتا به همتا) آشنا میشویم.
For more classes visit
www.snaptutorial.com
PLEASE CHECK ALL INCLUDED PRODUCTS IN THIS TUTORIAL AS SOME QUIZ MAY BE MISSING
CIS 333 Week 1 Discussion Providing Security Over Data
CIS 333 Week 2 Discussion Risk Management and Malicious Attacks
International Journal of Wireless & Mobile Networks (IJWMN) ijwmn
The world of Internet of Things (IoT) and ubiquitous computing lead the computing systems integrate sensors and handheld devices into a common platform to offer new services. Participatory Sensor Network (PSN) is one of such a network which is formed in an ad-hoc basis. The success of such network always depends on the quality of data shared by the participants. Privacy concern is one of the main reasons why an individual may not prefer to share their sensitive data. Not many research works have been performed to preserve the privacy of individual data in a PSN. On the other hand, Named Data Network (NDN), an instance of Information-Centric Network (ICN), is an alternative of TCP/IP that inherently considers the concern of security as opposed to TCP/IP. By default, NDN ensures the privacy of the data consumer but it fails to ensure the same for data provider. In this paper, we propose a ring signature based NDN to ensure the privacy of the data provider. Our proposed solution seems to be effective based on the performance and security analysis.
A Reliable Peer-to-Peer Platform for Adding New Node Using Trust Based Model IJECEIAES
In order to evaluate the trustworthiness of participating peers in unstructured peer-to-peer networks, Reputation aggregation methods are used in this method. Each and every peer of the network will collect the local scores of each transaction and will compute global scores by aggregating all the local scores with the help of global scores, each individual peer can interact with its suitable peers. But the existing method will not consider the score of the new peer. In this condition, requests are handled by existing peers who leads to failure in downloading process. To rectify this, NP-TRUST model is used to distribute the request to all peers including the newly joined peers. The proposed method is compared with gossip and DFR-TRUST model in Transaction Success rate and variation in file request.
Fusion of data from multiple sources is generating new information from existing data. Now users can access any information from inside or outside of the organization very easily. It helps to increase the user productivity and knowledge shared within the organization. But this leads to a new area of network security threat, “Inside Threat”. Now users can share critical information of organization to outside the organization if he/she has access to the information. The current network security tool cannot prevent the new threat. In this paper, we address this issue by “Building real time anomaly detection system based on users’ current behavior and previous behavior”.
Classification of Malware Attacks Using Machine Learning In Decision TreeCSCJournals
Predicting cyberattacks using machine learning has become imperative since cyberattacks have increased exponentially due to the stealthy and sophisticated nature of adversaries. To have situational awareness and achieve defence in depth, using machine learning for threat prediction has become a prerequisite for cyber threat intelligence gathering. Some approaches to mitigating malware attacks include the use of spam filters, firewalls, and IDS/IPS configurations to detect attacks. However, threat actors are deploying adversarial machine learning techniques to exploit vulnerabilities. This paper explores the viability of using machine learning methods to predict malware attacks and build a classifier to automatically detect and label an event as “Has Detection or No Detection”. The purpose is to predict the probability of malware penetration and the extent of manipulation on the network nodes for cyber threat intelligence. To demonstrate the applicability of our work, we use a decision tree (DT) algorithms to learn dataset for evaluation. The dataset was from Microsoft Malware threat prediction website Kaggle. We identify probably cyberattacks on smart grid, use attack scenarios to determine penetrations and manipulations. The results show that ML methods can be applied in smart grid cyber supply chain environment to detect cyberattacks and predict future trends.
Among different online attacks obstructing IT security,
Denial of Service (DoS) and Distributed Denial of Service (DDoS)
are the most devastating attack. It also put the security experts under
enormous pressure recently in finding efficient defiance methods.
DoS attack can be performed variously with diverse codes and tools
and can be launched form different OSI model layers. This paper
describes in details DoS and DDoS attack, and explains how different
types of attacks can be implemented and launched from different OSI
model layers. It provides a better understanding of these increasing
occurrences in order to improve
Evaluation of network intrusion detection using markov chainIJCI JOURNAL
Day today life internet threat has been increased significantly. There is a need to develop model in order to
maintain security of system. The most effective techniques are Intrusion Detection System (IDS).The
purpose of intrusion system through the security devices detect and deal with it. In this paper, a
mathematical approach is used effectively to predict and detect intrusion in the network. Here we discuss
about two algorithms ‘K-Means + Apriori’, a method which classify normal and abnormal activities in
computer network. In K-Means process, it partitions the training set into K-clusters using Euclidean
distance and introduce an outlier factor, then it build Apriori Algorithm to prune the data by removing
infrequent data in the database. Based on defined state the degree of incoming data is evaluated through
the experiment using sample DARPA2000 dataset, and achieves high detection performance in level of
attack in stages.
INFRINGEMENT PRECLUSION SYSTEM VIA SADEC: STEALTHY ATTACK DETECTION AND COUNT...ijp2p
In this paper we are providing a implementation details about simulated solution of stealthy packet drop
attack. Stealthy packet drop attack is a suite of four attack types, includes colluding collision, packet
misrouting, identity delegation and power control. Stealthy packet drop attacks disrupts the packet from
reaching to it’s destination through malicious behaviour. These attacks can be easily breakdown the
multi-hop wireless ad-hoc networks. Most widely preferred method for detecting attacks in wireless
network is behaviour based detection method. In this method a normal network overhears
communication from its neighbourhood. Here we are implementing a SADEC protocol which is
proposed solution of stealthy packet drop attacks. SADEC overlaid the base line local monitoring. In
base line local monitoring each neighbour maintains additional information about routing path also it
adds some checking responsibility to all its neighbours. SADEC proves more efficient than baseline local
monitoring to mitigate successfully all the stealthy attack types.
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
COLLUSION-TOLERABLE PRIVACY-PRESERVING SUM AND PRODUCT CALCULATION WITHOUT SE...Nexgen Technology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
COLLUSION-TOLERABLE PRIVACY-PRESERVING SUM AND PRODUCT CALCULATION WITHOUT SE...nexgentechnology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
For more classes visit
www.snaptutorial.com
PLEASE CHECK ALL INCLUDED PRODUCTS IN THIS TUTORIAL AS SOME QUIZ MAY BE MISSING
CIS 333 Week 1 Discussion Providing Security Over Data
CIS 333 Week 2 Discussion Risk Management and Malicious Attacks
International Journal of Wireless & Mobile Networks (IJWMN) ijwmn
The world of Internet of Things (IoT) and ubiquitous computing lead the computing systems integrate sensors and handheld devices into a common platform to offer new services. Participatory Sensor Network (PSN) is one of such a network which is formed in an ad-hoc basis. The success of such network always depends on the quality of data shared by the participants. Privacy concern is one of the main reasons why an individual may not prefer to share their sensitive data. Not many research works have been performed to preserve the privacy of individual data in a PSN. On the other hand, Named Data Network (NDN), an instance of Information-Centric Network (ICN), is an alternative of TCP/IP that inherently considers the concern of security as opposed to TCP/IP. By default, NDN ensures the privacy of the data consumer but it fails to ensure the same for data provider. In this paper, we propose a ring signature based NDN to ensure the privacy of the data provider. Our proposed solution seems to be effective based on the performance and security analysis.
A Reliable Peer-to-Peer Platform for Adding New Node Using Trust Based Model IJECEIAES
In order to evaluate the trustworthiness of participating peers in unstructured peer-to-peer networks, Reputation aggregation methods are used in this method. Each and every peer of the network will collect the local scores of each transaction and will compute global scores by aggregating all the local scores with the help of global scores, each individual peer can interact with its suitable peers. But the existing method will not consider the score of the new peer. In this condition, requests are handled by existing peers who leads to failure in downloading process. To rectify this, NP-TRUST model is used to distribute the request to all peers including the newly joined peers. The proposed method is compared with gossip and DFR-TRUST model in Transaction Success rate and variation in file request.
Fusion of data from multiple sources is generating new information from existing data. Now users can access any information from inside or outside of the organization very easily. It helps to increase the user productivity and knowledge shared within the organization. But this leads to a new area of network security threat, “Inside Threat”. Now users can share critical information of organization to outside the organization if he/she has access to the information. The current network security tool cannot prevent the new threat. In this paper, we address this issue by “Building real time anomaly detection system based on users’ current behavior and previous behavior”.
Classification of Malware Attacks Using Machine Learning In Decision TreeCSCJournals
Predicting cyberattacks using machine learning has become imperative since cyberattacks have increased exponentially due to the stealthy and sophisticated nature of adversaries. To have situational awareness and achieve defence in depth, using machine learning for threat prediction has become a prerequisite for cyber threat intelligence gathering. Some approaches to mitigating malware attacks include the use of spam filters, firewalls, and IDS/IPS configurations to detect attacks. However, threat actors are deploying adversarial machine learning techniques to exploit vulnerabilities. This paper explores the viability of using machine learning methods to predict malware attacks and build a classifier to automatically detect and label an event as “Has Detection or No Detection”. The purpose is to predict the probability of malware penetration and the extent of manipulation on the network nodes for cyber threat intelligence. To demonstrate the applicability of our work, we use a decision tree (DT) algorithms to learn dataset for evaluation. The dataset was from Microsoft Malware threat prediction website Kaggle. We identify probably cyberattacks on smart grid, use attack scenarios to determine penetrations and manipulations. The results show that ML methods can be applied in smart grid cyber supply chain environment to detect cyberattacks and predict future trends.
Among different online attacks obstructing IT security,
Denial of Service (DoS) and Distributed Denial of Service (DDoS)
are the most devastating attack. It also put the security experts under
enormous pressure recently in finding efficient defiance methods.
DoS attack can be performed variously with diverse codes and tools
and can be launched form different OSI model layers. This paper
describes in details DoS and DDoS attack, and explains how different
types of attacks can be implemented and launched from different OSI
model layers. It provides a better understanding of these increasing
occurrences in order to improve
Evaluation of network intrusion detection using markov chainIJCI JOURNAL
Day today life internet threat has been increased significantly. There is a need to develop model in order to
maintain security of system. The most effective techniques are Intrusion Detection System (IDS).The
purpose of intrusion system through the security devices detect and deal with it. In this paper, a
mathematical approach is used effectively to predict and detect intrusion in the network. Here we discuss
about two algorithms ‘K-Means + Apriori’, a method which classify normal and abnormal activities in
computer network. In K-Means process, it partitions the training set into K-clusters using Euclidean
distance and introduce an outlier factor, then it build Apriori Algorithm to prune the data by removing
infrequent data in the database. Based on defined state the degree of incoming data is evaluated through
the experiment using sample DARPA2000 dataset, and achieves high detection performance in level of
attack in stages.
INFRINGEMENT PRECLUSION SYSTEM VIA SADEC: STEALTHY ATTACK DETECTION AND COUNT...ijp2p
In this paper we are providing a implementation details about simulated solution of stealthy packet drop
attack. Stealthy packet drop attack is a suite of four attack types, includes colluding collision, packet
misrouting, identity delegation and power control. Stealthy packet drop attacks disrupts the packet from
reaching to it’s destination through malicious behaviour. These attacks can be easily breakdown the
multi-hop wireless ad-hoc networks. Most widely preferred method for detecting attacks in wireless
network is behaviour based detection method. In this method a normal network overhears
communication from its neighbourhood. Here we are implementing a SADEC protocol which is
proposed solution of stealthy packet drop attacks. SADEC overlaid the base line local monitoring. In
base line local monitoring each neighbour maintains additional information about routing path also it
adds some checking responsibility to all its neighbours. SADEC proves more efficient than baseline local
monitoring to mitigate successfully all the stealthy attack types.
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
COLLUSION-TOLERABLE PRIVACY-PRESERVING SUM AND PRODUCT CALCULATION WITHOUT SE...Nexgen Technology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
COLLUSION-TOLERABLE PRIVACY-PRESERVING SUM AND PRODUCT CALCULATION WITHOUT SE...nexgentechnology
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Privacy-preserving Data Mining in Industry: Practical Challenges and Lessons ...Krishnaram Kenthapadi
Preserving privacy of users is a key requirement of web-scale data mining applications and systems such as web search, recommender systems, crowdsourced platforms, and analytics applications, and has witnessed a renewed focus in light of recent data breaches and new regulations such as GDPR. In this tutorial, we will first present an overview of privacy breaches over the last two decades and the lessons learned, key regulations and laws, and evolution of privacy techniques leading to differential privacy definition / techniques. Then, we will focus on the application of privacy-preserving data mining techniques in practice, by presenting case studies such as Apple’s differential privacy deployment for iOS, Google’s RAPPOR, and LinkedIn Salary. We will also discuss various open source as well as commercial privacy tools, and conclude with open problems and challenges for data mining / machine learning community.
Technologies in Support of Big Data EthicsMark Underwood
As part of the NIST Big Data Public Working Group, we examine technologies that can support ethics in systems design. In particular, we review issues raised by the IEEE P7000 community regarding ethics for autonomous systems and robotics. Possible adaptations to the NBDPWG reference model are considered for the third and final version of SP1500.
PUBLIC INTEGRIYT AUDITING FOR SHARED DYNAMIC DATA STORAGE UNDER ONTIME GENERA...paperpublications3
Abstract: Nowadays verifying the result of the remote computation plays a crucial role in addressing in issue of trust. The outsourced data collection comes for multiple data sources to diagnose the originator of errors by allotting each data sources a unique secrete key which requires the inner product conformation to be performed under any two parties different keys. The proposed methods outperform AISM technique to minimize the running time. The multi-key setting is given different secrete keys, multiple data sources can be upload their data streams along with their respective verifiable homomorphic tag. The AISM consist of three novel join techniques depending on the ADS availability: (i) Authenticated Indexed Sort Merge Join (AISM), which utilizes a single ADS on the join attribute, (ii) Authenticated Index Merge Join (AIM) that requires an ADS (on the join attribute) for both relations, and (iii) Authenticated Sort Merge Join (ASM), which does not rely on any ADS. The client should allow choosing any portion in the data streams for queries. The communication between the client and server is independent of input size. The inner product evaluation can be performed by any two sources and the result can be verified by using the particular tag.
Keywords: Computation of outsourcing, Data Stream, Multiple Key, Homomorphic encryption.
Title: PUBLIC INTEGRIYT AUDITING FOR SHARED DYNAMIC DATA STORAGE UNDER ONTIME GENERATED MULTIPLE KEYS
Author: C. NISHA MALAR, M. S. BONSHIA BINU
ISSN 2350-1049
International Journal of Recent Research in Interdisciplinary Sciences (IJRRIS)
Paper Publications
Anonymity based privacy-preserving dataKamal Spring
In this paper, we propose an efficient anonymous data reporting protocol for participatory sensing, which provides strong privacy protection, data accuracy and generality. The protocol consists of two stages, namely slot reservation and message submission. In the slot reservation stage, a group of N participants cooperate to assign each member a message slot in a vector which is essentially a message submission schedule, in such a manner that each participant’s slot is oblivious to other members and the application server. In the message submission stage, each participant transmits an encoded data to the application server based on the slot information known only to herself, in such a way that the application server cannot link a data to a specific participant. With such a data reporting protocol, the link between the data and the participants is broken, and as a result, participant’s privacy is protected. We conduct theoretical analysis of the correctness and anonymity of our protocol, as well as experiments to demonstrate the efficiency in small-scale applications with periodic data sampling
Privacy Engineering: Enabling Mobility of Mental Health Services with Data Pr...CREST
This presentation describes privacy engineering for mobile health apps. it revealed that top-ranked apps lack fundamental data protection mechanisms, and that explicit and understandable consent in apps is needed for data access/sharing within or across organisations
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
Similar to Towards Statistical Queries over Distributed Private User Data (20)
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
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During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
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To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
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Towards Statistical Queries over Distributed Private User Data
1. Towards Statistical Queries over
Distributed Private User Data
R.Chen, A.Reznichenko, P.Francis – MPI-SWS, Germany
J.Gehrke – Cornell University, USA
Serafeim Chatzopoulos
M1258
schatz@di.uoa.gr
MDE519 – Distributed Systems
Instructor: Mema Roussopoulou
May 31,
2013
2. User Privacy
Towards Statistical Queries over Distributed Private User Data 2
User Data is exposed to organizations in many
ways.
Users are aware of their data being exposed.
Make a purchase in an online store.
Update a profile on a social network.
Users are unaware of their data exposure.
Third party trackers.
Smart phone Apps.
3. The “user-owned and operated” principle
Towards Statistical Queries over Distributed Private User Data 3
Personal data should be stored in a local host or a
cloud device under the user‟s control and is released
in a controlled, limited or noisy fashion.
Users must have the exclusive control of
their own data and must be able to share
data selectively or voluntarily.
4. Motivation and Problem
Towards Statistical Queries over Distributed Private User Data 4
Distributed private user data is important.
Analyst could use such data to
understand users‟ behaviors
discover their statistic patterns
evaluate proposed enhancements.
How to make statistical queries over such distributed
private user data while still preserving privacy?
5. Related Work
Towards Statistical Queries over Distributed Private User Data 5
Anonymization
Removes well-known personally identifiable
information(PPI).
Randomization
Adds random distortion values to user data.
k-anonymity, l-diversity, t-closeness
Differential Privacy
6. Differential Privacy
Towards Statistical Queries over Distributed Private User Data 6
Differential privacy adds noise to the output of a
computation (i.e., answer of query).
Hides the presence or absence of a record in the
dataset.
Makes no assumption about the adversary.
Some form of distributed differential privacy is
required…
7. Prior Distributed Differential Privacy Designs
Towards Statistical Queries over Distributed Private User Data 7
First design has a per-user computational load of
O(U).
Dwork et al. EUROCRYPT ‟06
Poor scalability
Following designs reduce per-user computational
load to O(1) by using expensive secret sharing
protocols.
Rastogi and Nath, SIGMOD ‟10 – Shi et al. NDSS ‟11
Not tolerate churn
Recent designs introduce two honest-but-curious
servers to collaboratively compute the query result.
Gotz and Nath, MSR-TR ‟11
Even a single malicious user can substantially distort
the query result.
8. Practical Distributed Differential Privacy System
(PDDP)
Towards Statistical Queries over Distributed Private User Data 8
Goals:
The differential private guarantee is always maintained for
every honest client.
Puts tight bound to the extent to which a malicious user
can distort query results.
The maximum absolute distortion in the final result is bounded
by the number of malicious users.
Operates at a large scale.
Millions of users.
Tolerates churn.
Not prevent results from being produced.
9. PDDP Components
Towards Statistical Queries over Distributed Private User Data 9
Analyst
Makes queries to the system
and collects answers.
Proxy
Adds differential private noise
to client‟s answers to preserve
privacy
Clients
Locally maintain their own data
and answer queries.
10. Security Assumptions (1/2)
Towards Statistical Queries over Distributed Private User Data 10
General Assumptions
Clients have the correct public keys for analyst and the
proxy.
Analyst and the proxy have the correct public keys for
each other.
Corresponding private keys are kept secure.
Analyst is potentially malicious (violating users‟
privacy)
Collude with other analysts.
Pretend to be multiple distinct analysts.
Take control of clients and use PDDP protocol to reveal
info.
Publish its collected answers.
Intercept and modify all messages.
11. Security Assumptions (2/2)
Towards Statistical Queries over Distributed Private User Data 11
Proxy is honest but curious (HbC)
Follows the specified protocol.
Tries to exploit additional info that can be learned in so
doing.
Does not collude with other components.
Clients are potentially malicious (distorting the
statistical results learned by analysts)
Have churn characteristics.
Limited resources for computation and data transmission.
Generate false or illegitimate answers.
Act as Sybils.
12. PDDP Key insights – Binary answer
Towards Statistical Queries over Distributed Private User Data 12
How to limit query result distortion?
Split answer‟s value into buckets.
Enforce a binary answer in each bucket.
Goldwasser-Micali (GM) bit-cryptosystem.
Example:
Query: “SELECT age FROM info WHERE gender=„m‟”
4 buckets: 0~12, 13~20, 21~59, and ≥60.
Answers: „1‟ or „0‟ per bucket
Malicious clients cannot substantially distort the query
result.
13. PDDP Key insights – Blind noise
Towards Statistical Queries over Distributed Private User Data 13
How to achieve differential privacy ?
Honest-but-curious proxy
Generates additional binary answers in each bucket as
differentially private noise.
If analyst publishes the final noisy result
proxy knows the noise added
can subtract noise from the publish result to get a noisy-free
result.
Solution: Proxy can only blindly add noise!
Proxy knows that the added noise is enough to achieve
differential privacy
Proxy does not know the exact noise added.
14. PDDP Workflow – Step 1
Towards Statistical Queries over Distributed Private User Data 14
Query Initialization
Analyst first issues
a query to the
Proxy.
Message consists of 4 items:
Query: SELECT age FROM info WHERE gender=„m‟
Buckets: 0∼12, 13∼20, 21∼59 and ≥60.
# clients queried (c): 1000
DP parameter (ε): 1.0
Controls tradeoff between accuracy of computation and strength of
its privacy guarantee.
15. PDDP Workflow – Step 2
Towards Statistical Queries over Distributed Private User Data 15
Query Forwarding
Select clients and
send them the
query.
Proxy:
rejects the query if c is too low or too high.
rejects the query if ε exceeds the max privacy level allowed.
selects c unique clients and send them the query, under the one
of the following policies:
Select c clients randomly and wait for them to connect.
Select the first c clients that connect.
16. PDDP Workflow – Step 3 (1/2)
Towards Statistical Queries over Distributed Private User Data 16
Client Response
Clients execute
the query and
send answers.
Client executes query over its local data and produces
answer:
„1‟ or „0‟ per bucket.
More than one bucket may contain a „1‟.
Per-bucket answer value is individually encrypted with the
analyst‟s public key. (GM cryptosystem)
17. PDDP Workflow – Step 3 (2/2)
Towards Statistical Queries over Distributed Private User Data 17
Goldwasser-Micali (GM) cryptosystem
Single-bit cryptosystem
Enforces binary answer in each bucket.
Very Efficient
XOR – homomorphic
E(a) * E(b) = E(a XOR b)
18. PDDP Workflow – Step 4
Towards Statistical Queries over Distributed Private User Data 18
Blind noise
addition
The proxy maintains a pool of additional binary
answers called coins and adds them as noise to
each bucket.
Coins must be unbiased.
Coins are encrypted with the analyst‟s public key.
In each bucket must be added n coins:
How to generate coins blindly?
19. Coin pool generation
Towards Statistical Queries over Distributed Private User Data 19
Straightforward approaches
Proxy generates coins
Curious proxy could know noise-free result
Clients generate coins
Malicious clients could generate biased coins
20. Collaborative coin generation
Towards Statistical Queries over Distributed Private User Data 20
Paper‟s approach
Each online client periodically generates an encrypted
unbiased coin E(oc) and sends it to the proxy
The proxy receives the coin and verifies the legitimacy of the
coin.
The proxy blindly re-flips the coin E(oc) by multiplying it with a
proxy‟s locally generated unbiased coin E(op) plus a modulo
operation.
E(oc) * E(op) mod m = E(oc XOR op),
where m is part of the analyst’s public key
The proxy stores the unbiased coin in the locally maintained
pool.
Proxy doesn‟t know the actual value of the generated unbiased
coin.
21. PDDP Workflow – Step 5
Towards Statistical Queries over Distributed Private User Data 21
Noisy answers to
analyst
Each bucket has clients answers + coins (noise)
After random delay the proxy shuffles the c + n values.
Prevents identification of a client based on the vector of „1‟ and „0‟ in its answer.
Finally, analyst
decrypts with its private key all encrypted binary values.
sums the plaintext values obtained.
obtains the noisy answer for the clients that fall within each bucket.
22. Practical Considerations (1/2)
Towards Statistical Queries over Distributed Private User Data 22
Utility of aggregate result
Depends on the amount of added noise.
The n coins added by the proxy and the analyst‟s adjustment on
the means of n/2 form a binomial distribution (approximation of
the normal distribution N(0, n/4) ).
Example :
c =106 , ε = 1.0
Given normal distribution in each bucket
68% probability that the noisy answer is 15.24 away from the true answer
95% probability that the noisy answer is 30.48 away from the true answer
99.7% probability that the noisy answer is 45.72 away from the true answer
23. Practical Considerations (2/2)
Towards Statistical Queries over Distributed Private User Data 23
Non-numeric Queries
Map query into a numeric query.
Example:
“Which website do you visit most often?”
Map each website the analyst wishes to learn into a numeric
value.
Large number of buckets – limit the answer to 5000 buckets.
Sybils
Design susceptible to Sybil attacks (single client can
masquerades multiple clients).
Proxy can limit the number of clients selected at a single IP
address for a given query.
24. Implementation and Deployment (1/2)
Towards Statistical Queries over Distributed Private User Data 24
Client
Firefox add-on
9600 lines of Java code
Information is stored in local SQLite storage
Web browsing activities
Certain online shopping activities
Certain ad interactions
Can be extended to capture any online activity
Every 5 min connects to the proxy to retrieve pending queries,
return answers and periodically generated coins.
25. Implementation and Deployment (2/2)
Towards Statistical Queries over Distributed Private User Data 25
Proxy
Web service on Tomcat 6.0.33
3600 lines of code
Proxy state in MySQL database.
Analyst
800 lines of code
Deployment
Correctness verified on a set of local machines.
600+ real clients
26. Comparison: “Paillier-based” design
Towards Statistical Queries over Distributed Private User Data 26
Honest-but-Curious Proxy
Paillier Cryptosystem
Additive homomorphism
Proxy can directly sum up all clients‟ encrypted binary
answers to get the encrypted sum of each bucket.
A single malicious client can distort substantially the
result
Use of zero-knowledge-proofs (ZKP) to ensure that
encrypted answers are „1‟ or „0‟.
Proxy knows exactly how much noise has been
added.
27. Evaluation (1/5)
Towards Statistical Queries over Distributed Private User Data 27
Client Performance
Clients encrypt a binary value for each bucket.
GM cryptosystem
Paillier cryptosystem
28. Evaluation (2/5)
Towards Statistical Queries over Distributed Private User Data 28
Proxy - Analyst Performance
Proxy
PDDP
One encryption and one homomorphic XOR for one unbiased coin.
Jacobi symbol checking on received coins and answer values
(faster than a decryption).
Paillier-based
One ZKP for each client answer in each bucket.
Homomorphically sum up all clients answers per bucket.
Add noise to each per-bucket total sum.
29. Evaluation (3/5)
Towards Statistical Queries over Distributed Private User Data 29
Proxy - Analyst Performance
Analyst
PDDP
Decrypt all encrypted values in each bucket.
Paillier-based
Decrypt one encrypted value in each bucket
30. Evaluation (4/5)
Towards Statistical Queries over Distributed Private User Data 30
Bandwidth overhead
In both systems, a client transmits an encrypted answer to
each bucket.
In PDDP, a client transmits periodically generated coin to the
proxy.
In Paillier-based, a client transmits a ZKP for each bucket.
Storage overhead
In PDDP, the proxy stores all clients‟ answer values for each
bucket plus the required number of coins.
In Paillier-based, proxy stores only one answer value per
bucket.
31. Evaluation (5/5)
Towards Statistical Queries over Distributed Private User Data 31
Querying the client deployment
Parameters
c = 250 (out of 600 clients)
ε = 5.0
clients are selected as they connect until 250 unique clients are queried or
24-hours expire.
These parameters result in 16 coins per bucket.
Ensure that a per bucket aggregate answer is within plus or minus 2, 4, 6
of the noisy-free answer with a probability of 68%, 95% and 99,7%
32. Future Work
Towards Statistical Queries over Distributed Private User Data 32
Support of statistical learning algorithms
Scalability of non-numeric queries
Bloom filters – map a large number of possible answers in
a small number of buckets.
Gather statistical data for a large-scale experiment.
Weaken proxy trust requirements.
Use of trusted hardware (TPM)
General: measure the actual privacy loss for
differential privacy.
33. Conclusion
Towards Statistical Queries over Distributed Private User Data 33
PDDP: Practical Distributed Differential Private
System
Scales well
Tolerates churn
Places tight bound on malicious user‟s capability.
Key insights
Binary answer in each bucket
Blind noise addition