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Differential Privacy and Applications

  • Tianqing Zhu
  • Gang Li
  • Wanlei Zhou
  • Philip S. Yu

Part of the Advances in Information Security book series (ADIS, volume 69)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu
    Pages 1-6
  3. Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu
    Pages 7-16
  4. Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu
    Pages 17-21
  5. Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu
    Pages 23-34
  6. Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu
    Pages 35-48
  7. Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu
    Pages 49-65
  8. Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu
    Pages 67-82
  9. Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu
    Pages 83-90
  10. Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu
    Pages 91-105
  11. Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu
    Pages 107-129
  12. Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu
    Pages 131-150
  13. Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu
    Pages 151-172
  14. Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu
    Pages 173-189
  15. Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu
    Pages 191-214
  16. Tianqing Zhu, Gang Li, Wanlei Zhou, Philip S. Yu
    Pages 215-222
  17. Back Matter
    Pages 223-235

About this book

Introduction

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.

Keywords

data analysis data mining data release differential policy location privacy machine learning privacy preserving recommender system differential privacy Differentially Private Data Publishing Differentially Private Data Analysis Data Sharing Private Learning Statistical Learning online social networks Privacy Security Cryptography

Authors and affiliations

  1. 1.Deakin UniversityMelbourneAustralia
  2. 2.Deakin UniversityMelbourneAustralia
  3. 3.Deakin UniversityMelbourneAustralia
  4. 4.University of Illinois at ChicagoChicagoUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-62004-6
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-62002-2
  • Online ISBN 978-3-319-62004-6
  • Series Print ISSN 1568-2633
  • Buy this book on publisher's site
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