9783319620022-3319620029-Differential Privacy and Applications (Advances in Information Security, 69)

Differential Privacy and Applications (Advances in Information Security, 69)

ISBN-13: 9783319620022
ISBN-10: 3319620029
Edition: 1st ed. 2017
Author: Zhu
Publication date: 2017
Publisher: Springer
Format: Hardcover 252 pages
FREE US shipping
Buy

From $41.70

Book details

ISBN-13: 9783319620022
ISBN-10: 3319620029
Edition: 1st ed. 2017
Author: Zhu
Publication date: 2017
Publisher: Springer
Format: Hardcover 252 pages

Summary

Differential Privacy and Applications (Advances in Information Security, 69) (ISBN-13: 9783319620022 and ISBN-10: 3319620029), written by authors Zhu, was published by Springer in 2017. With an overall rating of 4.5 stars, it's a notable title among other AI & Machine Learning (Data Mining, Databases & Big Data, Internet, Groupware, & Telecommunications, Networking & Cloud Computing, Network Security, Security & Encryption, Internet & Social Media, Privacy & Online Safety, Computer Science) books. You can easily purchase or rent Differential Privacy and Applications (Advances in Information Security, 69) (Hardcover) from BooksRun, along with many other new and used AI & Machine Learning books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $0.3.

Description

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.
Rate this book Rate this book

We would LOVE it if you could help us and other readers by reviewing the book