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| Full Title | Preserving Privacy Against Side-Channel Leaks From Data Publishing to Web Applications |
|---|---|
| Author(s) | Wen Ming Liu, Lingyu Wang |
| Edition | |
| ISBN | 9783319426440, 9783319426426 |
| Publisher | Springer |
| Format | PDF and EPUB |
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.
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