Differential Privacy And Applications
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Differential Privacy And Applications
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Author : Tianqing Zhu
language : en
Publisher: Springer
Release Date : 2017-08-22
Differential Privacy And Applications written by Tianqing Zhu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-22 with Computers categories.
This book focuses on differential privacy and its application with an emphasis on technical and application aspects. This book also presents the most recent research on differential privacy with a theory perspective. It provides an approachable strategy for researchers and engineers to implement differential privacy in real world applications. Early chapters are focused on two major directions, differentially private data publishing and differentially private data analysis. Data publishing focuses on how to modify the original dataset or the queries with the guarantee of differential privacy. Privacy data analysis concentrates on how to modify the data analysis algorithm to satisfy differential privacy, while retaining a high mining accuracy. The authors also introduce several applications in real world applications, including recommender systems and location privacy Advanced level students in computer science and engineering, as well as researchers and professionals working in privacy preserving, data mining, machine learning and data analysis will find this book useful as a reference. Engineers in database, network security, social networks and web services will also find this book useful.
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.
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.
Tutorials On The Foundations Of Cryptography
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Author : Yehuda Lindell
language : en
Publisher: Springer
Release Date : 2017-04-05
Tutorials On The Foundations Of Cryptography written by Yehuda Lindell and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-04-05 with Computers categories.
This is a graduate textbook of advanced tutorials on the theory of cryptography and computational complexity. In particular, the chapters explain aspects of garbled circuits, public-key cryptography, pseudorandom functions, one-way functions, homomorphic encryption, the simulation proof technique, and the complexity of differential privacy. Most chapters progress methodically through motivations, foundations, definitions, major results, issues surrounding feasibility, surveys of recent developments, and suggestions for further study. This book honors Professor Oded Goldreich, a pioneering scientist, educator, and mentor. Oded was instrumental in laying down the foundations of cryptography, and he inspired the contributing authors, Benny Applebaum, Boaz Barak, Andrej Bogdanov, Iftach Haitner, Shai Halevi, Yehuda Lindell, Alon Rosen, and Salil Vadhan, themselves leading researchers on the theory of cryptography and computational complexity. The book is appropriate for graduate tutorials and seminars, and for self-study by experienced researchers, assuming prior knowledge of the theory of cryptography.
Data And Applications Security And Privacy Xxxv
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Author : Ken Barker
language : en
Publisher: Springer Nature
Release Date : 2021-07-14
Data And Applications Security And Privacy Xxxv written by Ken Barker and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-14 with Computers categories.
This book constitutes the refereed proceedings of the 35th Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy, DBSec 2021, held in Calgary, Canada, in July 2021.* The 15 full papers and 8 short papers presented were carefully reviewed and selected from 45 submissions. The papers present high-quality original research from academia, industry, and government on theoretical and practical aspects of information security. They are organized in topical sections named differential privacy, cryptology, machine learning, access control and others. *The conference was held virtually due to the COVID-19 pandemic.
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 Computer science 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. After motivating and discussing the meaning of differential privacy, the preponderance of this monograph is devoted to fundamental techniques for achieving differential privacy, and 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 astonishingly powerful computational results, there are still fundamental limitations -- not just on what can be achieved with differential privacy but on what can be achieved with any method that protects against a complete breakdown in privacy. 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.
Advances In Cryptology Crypto 2009
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Author : Shai Halevi
language : en
Publisher: Springer
Release Date : 2009-08-18
Advances In Cryptology Crypto 2009 written by Shai Halevi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-08-18 with Computers categories.
This book constitutes the refereed proceedings of the 29th Annual International Cryptology Conference, CRYPTO 2009, held in Santa Barbara, CA, USA in August 2009. The 38 revised full papers presented were carefully reviewed and selected from 213 submissions. Addressing all current foundational, theoretical and research aspects of cryptology, cryptography, and cryptanalysis as well as advanced applications, the papers are organized in topical sections on key leakage, hash-function cryptanalysis, privacy and anonymity, interactive proofs and zero-knowledge, block-cipher cryptanalysis, modes of operation, elliptic curves, cryptographic hardness, merkle puzzles, cryptography in the physical world, attacks on signature schemes, secret sharing and secure computation, cryptography and game-theory, cryptography and lattices, identity-based encryption and cryptographers’ toolbox.
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.
Handbook On Using Administrative Data For Research And Evidence Based Policy
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Author : Shawn Cole
language : en
Publisher: Abdul Latif Jameel Poverty Action Lab
Release Date : 2021
Handbook On Using Administrative Data For Research And Evidence Based Policy written by Shawn Cole and has been published by Abdul Latif Jameel Poverty Action Lab this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
This Handbook intends to inform Data Providers and researchers on how to provide privacy-protected access to, handle, and analyze administrative data, and to link them with existing resources, such as a database of data use agreements (DUA) and templates. Available publicly, the Handbook will provide guidance on data access requirements and procedures, data privacy, data security, property rights, regulations for public data use, data architecture, data use and storage, cost structure and recovery, ethics and privacy-protection, making data accessible for research, and dissemination for restricted access use. The knowledge base will serve as a resource for all researchers looking to work with administrative data and for Data Providers looking to make such data available.
Production Of Categorical Data Verifying Differential Privacy
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Author : Héber Hwang Arcolezi
language : en
Publisher:
Release Date : 2022
Production Of Categorical Data Verifying Differential Privacy written by Héber Hwang Arcolezi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Private and public organizations regularly collect and analyze digitalized data about their associates, volunteers, clients, etc. However, because most personal data are sensitive, there is a key challenge in designing privacy-preserving systems to comply with data privacy laws, e.g., the General Data Protection Regulation. Differential privacy (DP) is a formal definition that allows quantifying the privacy-utility trade-off. With the local DP (LDP) model, users can sanitize their data locally before transmitting it to the server.The objective of this thesis is thus two-fold: O1) To improve the utility and privacy of LDP protocols for frequency estimation, which is fundamental to statistical learning. And O2) To propose privacy-preserving systems for data mining tasks with DP guarantees.For O1, we first tackled the problem from two multiple perspectives, i.e., multiple attributes and multiple collections throughout time (longitudinal studies), while focusing on utility. Secondly, we focused our attention on the multiple attributes aspect only, in which we proposed a solution focusing on privacy while preserving utility. In both cases, we demonstrate through analytical and experimental validations the advantages of our proposed solutions over state-of-the-art protocols.For O2, we proposed systems based on machine learning (ML) to solve real-world problems while ensuring DP guarantees. Indeed, we mainly used the input data perturbation setting from the privacy-preserving data mining literature. This is the situation in which the whole dataset is perturbed independently and, thus, we implemented LDP algorithms from the perspective of the centralized data owner. In all cases, we concluded that differentially private ML models achieve nearly the same performance as non-private ones.