Preserving Privacy Against Side-Channel Leaks

<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. </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. </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>