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Preserving Privacy Against Side-Channel Leaks : From Data Publishing to Web Applications / by Wen Ming Liu, Lingyu Wang.

SpringerLink Books Computer Science (2011-2024) Available online

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Format:
Book
Author/Creator:
Liu, Wenming, author.
Wang, Lingyu, author.
Contributor:
SpringerLink (Online service)
Series:
Computer Science (Springer-11645)
Advances in information security 1568-2633 ; 68.
Advances in Information Security, 1568-2633 ; 68
Language:
English
Subjects (All):
Computer security.
Data encryption (Computer science).
Computers.
Computer networks.
Systems and Data Security.
Cryptology.
Information Systems and Communication Service.
Computer Communication Networks.
Local Subjects:
Systems and Data Security.
Cryptology.
Information Systems and Communication Service.
Computer Communication Networks.
Physical Description:
1 online resource (XIII, 142 pages) : 19 illustrations, 1 illustrations in color.
Edition:
First edition 2016.
Contained In:
Springer eBooks
Place of Publication:
Cham : Springer International Publishing : Imprint: Springer, 2016.
System Details:
text file PDF
Summary:
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 strategy independent of data utility measures and syntactic privacy properties before discussing an extended approach to improve the efficiency. Next, the book explores privacy-preserving traffic padding in Web applications, first via a model to quantify privacy and cost and then by introducing randomness to provide background knowledge-resistant privacy guarantee. Finally, the book considers privacy-preserving smart metering by proposing a light-weight approach to simultaneously preserving users' privacy and ensuring billing accuracy. Designed for researchers and professionals, this book is also suitable for advanced-level students interested in privacy, algorithms, or web applications.
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.
Other Format:
Printed edition:
ISBN:
978-3-319-42644-0
9783319426440
Access Restriction:
Restricted for use by site license.

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