Preserving Privacy Against Side-Channel Leaks: From Data Publishing to Web Applications

ISBN-13: 9783319826264 / Angielski / Miękka / 2018 / 142 str.

Wen Ming Liu; Lingyu Wang

Spis treści

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.

Blurb

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.