Central Library

Normal view MARC view ISBD view

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

By: Liu, Wen Ming [author.].
Contributor(s): Wang, Lingyu [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Advances in Information Security: 68Publisher: Cham : Springer International Publishing : Imprint: Springer, 2016Description: XIII, 142 p. 19 illus., 1 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319426440.Subject(s): Computer science | Computer communication systems | Computer security | Data encryption (Computer science) | Computers | Computer Science | Systems and Data Security | Data Encryption | Information Systems and Communication Service | Computer Communication NetworksAdditional physical formats: Printed edition:: No titleDDC classification: 005.8 Online resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: 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.
    average rating: 0.0 (0 votes)
No physical items for this record

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.

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.

There are no comments for this item.

Log in to your account to post a comment.