Preserving Privacy Against Side-Channel Leaks
From Data Publishing to Web Applications
Nonfiction, Computers, Networking & Communications, Computer Security, Operating Systems, Application Software
| Author: | Wen Ming Liu, Lingyu Wang | ISBN: | 9783319426440 |
| Publisher: | Springer International Publishing | Publication: | August 24, 2016 |
| Imprint: | Springer | Language: | English |
| Author: | Wen Ming Liu, Lingyu Wang |
| ISBN: | 9783319426440 |
| Publisher: | Springer International Publishing |
| Publication: | August 24, 2016 |
| Imprint: | Springer |
| Language: | English |
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.
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.
