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Preserving privacy against side-chan...
~
Liu, Wen Ming.
Preserving privacy against side-channel leaks = from data publishing to web applications /
Record Type:
Language materials, printed : Monograph/item
Title/Author:
Preserving privacy against side-channel leaks/ by Wen Ming Liu, Lingyu Wang.
Reminder of title:
from data publishing to web applications /
Author:
Liu, Wen Ming.
other author:
Wang, Lingyu.
Published:
Cham :Springer International Publishing : : 2016.,
Description:
xiii, 142 p. :ill., digital ; : 24 cm.;
Contained By:
Springer eBooks
Subject:
Computer security. -
Online resource:
http://dx.doi.org/10.1007/978-3-319-42644-0
ISBN:
9783319426440
Preserving privacy against side-channel leaks = from data publishing to web applications /
Liu, Wen Ming.
Preserving privacy against side-channel leaks
from data publishing to web applications /[electronic resource] :by Wen Ming Liu, Lingyu Wang. - Cham :Springer International Publishing :2016. - xiii, 142 p. :ill., digital ;24 cm. - Advances in information security,v.681568-2633 ;. - Advances in information security ;50..
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.
ISBN: 9783319426440
Standard No.: 10.1007/978-3-319-42644-0doiSubjects--Topical Terms:
557122
Computer security.
LC Class. No.: QA76.9.A25
Dewey Class. No.: 005.8
Preserving privacy against side-channel leaks = from data publishing to web applications /
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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.
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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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