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Preserving Privacy Against Side-Channel Leaks From Data Publishing to Web Applications

This book offers a novel approach to data privacy by unifying side-channel attacks within a general conceptual framework. This book then applies the framework in three concrete domains. First, the book examines privacy-preserving data publishing with publicly-known algorithms, studying a generic str...

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Bibliographic Details
Main Authors: Liu, Wen Ming, Wang, Lingyu (Author)
Format: eBook
Language:English
Published: Cham Springer International Publishing 2016, 2016
Edition:1st ed. 2016
Series:Advances in Information Security
Subjects:
Computer Communication Networks
Cryptography
Computer Networks 
Data Protection
Data Encryption (computer Science)
Cryptology
Data And Information Security
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
Table of Contents:
  • Introduction
  • Related Work
  • Data Publishing: Trading off Privacy with Utility through the k-Jump Strategy
  • Data Publishing: A Two-Stage Approach to Improving Algorithm Efficiency
  • Web Applications: k-Indistinguishable Traffic Padding
  • Web Applications: Background-Knowledge Resistant Random Padding
  • Smart Metering: Inferences of Appliance Status from Fine-Grained Readings
  • The Big Picture: A Generic Model of Side-Channel Leaks
  • Conclusion

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