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Preserving Privacy Against Side-Channel Leaks

From Data Publishing to Web Applications

Produktform: Buch / Einband - fest (Hardcover)

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. weiterlesen

Dieser Artikel gehört zu den folgenden Serien

Sprache(n): Englisch

ISBN: 978-3-319-42642-6 / 978-3319426426 / 9783319426426

Verlag: Springer International Publishing

Erscheinungsdatum: 19.10.2016

Seiten: 142

Auflage: 1

Autor(en): Lingyu Wang, Wen Ming Liu

106,99 € inkl. MwSt.
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