Preserving Privacy Against Side-Channel Leaks

From Data Publishing to Web Applications
Softcover Nachdruck of the original 1. Auflage 2016
ISBN: 978-3-319-82626-4
Verlag: Springer International Publishing

Buch, Englisch, Band 68, 142 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 2467 g

Reihe: Advances in Information Security

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.

Zielgruppe

Research

Autoren/Hrsg.

Wang, Lingyu

Liu, Wen Ming

Fachgebiete

• Mathematik | Informatik EDV | Informatik Technische Informatik Computersicherheit Kryptographie, Datenverschlüsselung

Inhaltsverzeichnis

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.