Preserving Privacy Against Side-Channel Leaks : From Data Publishing to Web Applications / by Wen Ming Liu, Lingyu Wang

By:

Liu, Wen Ming [aut]

Contributor(s):

Wang, Lingyu [oth]

Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Advances in Information Security ; 68 | SpringerLink BücherPublisher: Cham ; s.l. : Springer International Publishing, 2016Description: Online-Ressource (XIII, 142 p. 19 illus., 1 illus. in color, online resource)ISBN:

9783319426440

Subject(s):

Additional physical formats: 9783319426426 | Druckausg.: 978-3-319-42642-6 LOC classification:

QA76.9.A25

DOI: DOI: 10.1007/978-3-319-42644-0Online resources:

Summary: Introduction -- Related Work -- Data Publishing: Trading off Privacy with Utility through the k-Jump Strategy -- Data Publishing: A Two-Stage Approach to Improving Algorithm Efficiency -- Web Applications: k-Indistinguishable Traffic Padding -- Web Applications: Background-Knowledge Resistant Random Padding -- Smart Metering: Inferences of Appliance Status from Fine-Grained Readings -- The Big Picture: A Generic Model of Side-Channel Leaks -- Conclusion.Summary: 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.PPN: PPN: 165866339XPackage identifier: Produktsigel: ZDB-2-SEB | ZDB-2-SXCS | ZDB-2-SCS

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