Wen Ming Liu, Lingyu Wang

Preserving Privacy Against Side-Channel Leaks (eBook, PDF)

From Data Publishing to Web Applications

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 142
• Erscheinungstermin: 24. August 2016
• Englisch
• ISBN-13: 9783319426440
• Artikelnr.: 46936516

Inhaltsangabe

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.