Table of contents
About this book
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.
Data privacy Data publishing Privacy preservation Side-channel attack Public algorithm k-Anonymity I-Diversity Traffic padding Web application Smart metering
- DOI https://doi.org/10.1007/978-3-319-42644-0
- Copyright Information Springer International Publishing Switzerland 2016
- Publisher Name Springer, Cham
- eBook Packages Computer Science
- Print ISBN 978-3-319-42642-6
- Online ISBN 978-3-319-42644-0
- Series Print ISSN 1568-2633
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