Differential Privacy For Databases
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Differential Privacy For Databases
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Author : Joseph P Near
language : en
Publisher:
Release Date : 2021-07-22
Differential Privacy For Databases written by Joseph P Near and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-22 with categories.
This book provides a database researcher or designer a complete, yet concise, overview of differential privacy and its deployment in database systems.
The Algorithmic Foundations Of Differential Privacy
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Author : Cynthia Dwork
language : en
Publisher:
Release Date : 2014
The Algorithmic Foundations Of Differential Privacy written by Cynthia Dwork and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Computers categories.
The problem of privacy-preserving data analysis has a long history spanning multiple disciplines. As electronic data about individuals becomes increasingly detailed, and as technology enables ever more powerful collection and curation of these data, the need increases for a robust, meaningful, and mathematically rigorous definition of privacy, together with a computationally rich class of algorithms that satisfy this definition. Differential Privacy is such a definition. The Algorithmic Foundations of Differential Privacy starts out by motivating and discussing the meaning of differential privacy, and proceeds to explore the fundamental techniques for achieving differential privacy, and the application of these techniques in creative combinations, using the query-release problem as an ongoing example. A key point is that, by rethinking the computational goal, one can often obtain far better results than would be achieved by methodically replacing each step of a non-private computation with a differentially private implementation. Despite some powerful computational results, there are still fundamental limitations. Virtually all the algorithms discussed herein maintain differential privacy against adversaries of arbitrary computational power -- certain algorithms are computationally intensive, others are efficient. Computational complexity for the adversary and the algorithm are both discussed. The monograph then turns from fundamentals to applications other than query-release, discussing differentially private methods for mechanism design and machine learning. The vast majority of the literature on differentially private algorithms considers a single, static, database that is subject to many analyses. Differential privacy in other models, including distributed databases and computations on data streams, is discussed. The Algorithmic Foundations of Differential Privacy is meant as a thorough introduction to the problems and techniques of differential privacy, and is an invaluable reference for anyone with an interest in the topic.
Privacy Preserving Data Publishing
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Author : Bee-Chung Chen
language : en
Publisher: Now Publishers Inc
Release Date : 2009
Privacy Preserving Data Publishing written by Bee-Chung Chen and has been published by Now Publishers Inc this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Business & Economics categories.
This book is dedicated to those who have something to hide. It is a book about "privacy preserving data publishing" -- the art of publishing sensitive personal data, collected from a group of individuals, in a form that does not violate their privacy. This problem has numerous and diverse areas of application, including releasing Census data, search logs, medical records, and interactions on a social network. The purpose of this book is to provide a detailed overview of the current state of the art as well as open challenges, focusing particular attention on four key themes: RIGOROUS PRIVACY POLICIES Repeated and highly-publicized attacks on published data have demonstrated that simplistic approaches to data publishing do not work. Significant recent advances have exposed the shortcomings of naive (and not-so-naive) techniques. They have also led to the development of mathematically rigorous definitions of privacy that publishing techniques must satisfy; METRICS FOR DATA UTILITY While it is necessary to enforce stringent privacy policies, it is equally important to ensure that the published version of the data is useful for its intended purpose. The authors provide an overview of diverse approaches to measuring data utility; ENFORCEMENT MECHANISMS This book describes in detail various key data publishing mechanisms that guarantee privacy and utility; EMERGING APPLICATIONS The problem of privacy-preserving data publishing arises in diverse application domains with unique privacy and utility requirements. The authors elaborate on the merits and limitations of existing solutions, based on which we expect to see many advances in years to come.
Privacy Preserving Data Mining
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Author : Jaideep Vaidya
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-11-29
Privacy Preserving Data Mining written by Jaideep Vaidya and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-11-29 with Computers categories.
Privacy preserving data mining implies the "mining" of knowledge from distributed data without violating the privacy of the individual/corporations involved in contributing the data. This volume provides a comprehensive overview of available approaches, techniques and open problems in privacy preserving data mining. Crystallizing much of the underlying foundation, the book aims to inspire further research in this new and growing area. Privacy Preserving Data Mining is intended to be accessible to industry practitioners and policy makers, to help inform future decision making and legislation, and to serve as a useful technical reference.
Privacy Big Data And The Public Good
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Author : Julia Lane
language : en
Publisher: Cambridge University Press
Release Date : 2014-06-09
Privacy Big Data And The Public Good written by Julia Lane and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-09 with Computers categories.
Data access is essential for serving the public good. This book provides new frameworks to address the resultant privacy issues.
Privacy And Security Policies In Big Data
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Author : Sharvari Tamane
language : en
Publisher: Information Science Reference
Release Date : 2017
Privacy And Security Policies In Big Data written by Sharvari Tamane and has been published by Information Science Reference this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Big data categories.
"This book covers the fundamental concepts of big data management and analytics along with recent research development in big data. It also includes various real time/offline applications and case studies in the field of engineering, computer science, information security, cloud computing with modern tools and technologies used"--
Privacy In Statistical Databases
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Author : Josep Domingo-Ferrer
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-09-09
Privacy In Statistical Databases written by Josep Domingo-Ferrer and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-09-09 with Computers categories.
This book constitutes the proceedings of the International Conference on Privacy in Statistical Databases held in Corfu, Greece, in September 2010.
Exploring The Boundaries Of Big Data
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Author : Bart van der Sloot
language : en
Publisher:
Release Date : 2016
Exploring The Boundaries Of Big Data written by Bart van der Sloot and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with Big data categories.
In the investigation Exploring the Boundaries of Big Data The Netherlands Scientific Council for Government Policy (WRR) offers building blocks for developing a regulatory approach to Big Data.
Differential Privacy
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Author : Ninghui Li
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2016-10-26
Differential Privacy written by Ninghui Li and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-26 with Computers categories.
Over the last decade, differential privacy (DP) has emerged as the de facto standard privacy notion for research in privacy-preserving data analysis and publishing. The DP notion offers strong privacy guarantee and has been applied to many data analysis tasks. This Synthesis Lecture is the first of two volumes on differential privacy. This lecture differs from the existing books and surveys on differential privacy in that we take an approach balancing theory and practice. We focus on empirical accuracy performances of algorithms rather than asymptotic accuracy guarantees. At the same time, we try to explain why these algorithms have those empirical accuracy performances. We also take a balanced approach regarding the semantic meanings of differential privacy, explaining both its strong guarantees and its limitations. We start by inspecting the definition and basic properties of DP, and the main primitives for achieving DP. Then, we give a detailed discussion on the the semantic privacy guarantee provided by DP and the caveats when applying DP. Next, we review the state of the art mechanisms for publishing histograms for low-dimensional datasets, mechanisms for conducting machine learning tasks such as classification, regression, and clustering, and mechanisms for publishing information to answer marginal queries for high-dimensional datasets. Finally, we explain the sparse vector technique, including the many errors that have been made in the literature using it. The planned Volume 2 will cover usage of DP in other settings, including high-dimensional datasets, graph datasets, local setting, location privacy, and so on. We will also discuss various relaxations of DP.