Differential Privacy and Applications (Hardcover, 1st ed. 2017)

This book focuses on differential privacy and its application with an emphasis on technical and application aspects. This book also presents the most recent research on differential privacy with a theory perspective. It provides an approachable strategy for researchers and engineers to implement differential privacy in real world applications. Early chapters are focused on two major directions, differentially private data publishing and differentially private data analysis. Data publishing focuses on how to modify the original dataset or the queries with the guarantee of differential privacy. Privacy data analysis concentrates on how to modify the data analysis algorithm to satisfy differential privacy, while retaining a high mining accuracy. The authors also introduce several applications in real world applications, including recommender systems and location privacy Advanced level students in computer science and engineering, as well as researchers and professionals working in privacy preserving, data mining, machine learning and data analysis will find this book useful as a reference. Engineers in database, network security, social networks and web services will also find this book useful.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Advances in Information Security, 69
Release date:	September 2017
First published:	2017
Authors:	Tianqing Zhu • Wanlei Zhou • Gang Li • Philip S. Yu
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	235
Edition:	1st ed. 2017
ISBN-13:	978-3-319-62002-2
Categories:	Books > Computing & IT > Social & legal aspects of computing > Privacy & data protection Books > Computing & IT > Applications of computing > Databases > General
LSN:	3-319-62002-9
Barcode:	9783319620022

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