Preserving Privacy Against Side-Channel Leaks
Produktnummer:
188e29298649a04e48b8767817ae09405a
| Autor: | Liu, Wen Ming Wang, Lingyu |
|---|---|
| Themengebiete: | Data privacy Data publishing I-Diversity Privacy preservation Public algorithm Side-channel attack Smart metering Traffic padding Web application k-Anonymity |
| Veröffentlichungsdatum: | 19.10.2016 |
| EAN: | 9783319426426 |
| Sprache: | Englisch |
| Seitenzahl: | 142 |
| Produktart: | Gebunden |
| Verlag: | Springer International Publishing |
| Untertitel: | From Data Publishing to Web Applications |
Produktinformationen "Preserving Privacy Against Side-Channel Leaks"
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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