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.