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Preserving Privacy Against Side-Channel Leaks: From Data Publishing to Web Applications: Advances in Information Security, cartea 68

Autor Wen Ming Liu, Lingyu Wang
en Limba Engleză Hardback – 19 oct 2016
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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Specificații

ISBN-13: 9783319426426
ISBN-10: 3319426427
Pagini: 170
Ilustrații: XIII, 142 p. 19 illus., 1 illus. in color.
Dimensiuni: 155 x 235 x 11 mm
Greutate: 0.4 kg
Ediția:1st ed. 2016
Editura: Springer International Publishing
Colecția Springer
Seria Advances in Information Security

Locul publicării:Cham, Switzerland

Cuprins

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.

Textul de pe ultima copertă

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 interestedin privacy, algorithms, or web applications.

Caracteristici

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