Preserving Privacy Against Side-Channel Leaks

From Data Publishing to Web Applications

Nonfiction, Computers, Networking & Communications, Computer Security, Operating Systems, Application Software
Cover of the book Preserving Privacy Against Side-Channel Leaks by Wen Ming Liu, Lingyu Wang, Springer International Publishing
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Author: Wen Ming Liu, Lingyu Wang ISBN: 9783319426440
Publisher: Springer International Publishing Publication: August 24, 2016
Imprint: Springer Language: English
Author: Wen Ming Liu, Lingyu Wang
ISBN: 9783319426440
Publisher: Springer International Publishing
Publication: August 24, 2016
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

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