Overview
Part of the book series: Synthesis Lectures on Information Security, Privacy, and Trust (SLISPT)
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About this book
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.
Table of contents (7 chapters)
Authors and Affiliations
About the authors
Dong Su is a Ph.D. student in the Computer Science Department at PurdueUniversity. He received a Bachelor's degree in software engineering from Tianjin University in 2005 and a Master's degree in computer science from the University of Chinese Academy of Sciences in 2010. He entered Purdue University in the Fall of 2010, and is working under the supervision of Dr. Ninghui Li on the topic of differentially private data publishing for data analysis. His research interests are in data privacy, information security, machine learning, and database management systems.
Weining Yang is a Ph.D. student in the Department of Computer Science at Purdue University. He attended Tsinghua University and graduated with a Bachelor's degree in computer science in 2011. He entered Purdue University in the Fall of 2011, and worked under the supervision of Dr. Ninghui Li on the topic of differentially private data publishing and password-based authentication. His research interests are in security and privacy, in particular private data publishing and user authentication.
Bibliographic Information
Book Title: Differential Privacy
Book Subtitle: From Theory to Practice
Authors: Ninghui Li, Min Lyu, Dong Su, Weining Yang
Series Title: Synthesis Lectures on Information Security, Privacy, and Trust
DOI: https://doi.org/10.1007/978-3-031-02350-7
Publisher: Springer Cham
eBook Packages: Synthesis Collection of Technology (R0), eBColl Synthesis Collection 7
Copyright Information: Springer Nature Switzerland AG 2017
Softcover ISBN: 978-3-031-01222-8Published: 26 October 2016
eBook ISBN: 978-3-031-02350-7Published: 31 May 2022
Series ISSN: 1945-9742
Series E-ISSN: 1945-9750
Edition Number: 1
Number of Pages: XIII, 124
Topics: Systems and Data Security