Details

Preserving Privacy Against Side-Channel Leaks


Preserving Privacy Against Side-Channel Leaks

From Data Publishing to Web Applications
Advances in Information Security, Band 68

von: Wen Ming Liu, Lingyu Wang

96,29 €

Verlag: Springer
Format: PDF
Veröffentl.: 24.08.2016
ISBN/EAN: 9783319426440
Sprache: englisch

Dieses eBook enthält ein Wasserzeichen.

Beschreibungen

<div>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.&nbsp;</div><div>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.&nbsp;</div><div>Designed for researchers and professionals, this book is also suitable for advanced-level students interested in privacy, algorithms, or web applications.</div><div><br></div>
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.
<div>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.&nbsp;</div><div>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.&nbsp;</div><div>Designed for researchers and professionals, this book is also suitable for advanced-level students interestedin privacy, algorithms, or web applications.</div>
Provides readers with insights into three important data privacy domains: data publishing, Web application, and smart metering Presents the similarities between seemingly different side-channels attacks in various domains Reveals promising future directions towards generic privacy solutions that are resistant to side channel attacks Includes supplementary material: sn.pub/extras

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