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Preserving privacy against side-chan...

Liu, Wen Ming.

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Preserving privacy against side-channel leaks = from data publishing to web applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Preserving privacy against side-channel leaks/ by Wen Ming Liu, Lingyu Wang.
其他題名:	from data publishing to web applications /
作者:	Liu, Wen Ming.
其他作者:	Wang, Lingyu.
出版者:	Cham :Springer International Publishing : : 2016.,
面頁冊數:	xiii, 142 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer eBooks
標題:	Computer security. -
電子資源:	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 leaksfrom 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 ;55..

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:

540555
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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505 0 $a 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.
520 $a 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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