Privacy-preserving machine learning / Jin Li, Ping Li, Zheli Liu, Xiaofeng Chen, Tong Li.
2022
Q325.5
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Details
Title
Privacy-preserving machine learning / Jin Li, Ping Li, Zheli Liu, Xiaofeng Chen, Tong Li.
Author
Li, Jin, author.
ISBN
9789811691393 (electronic bk.)
9811691398 (electronic bk.)
9789811691386 (print)
981169138X
9811691398 (electronic bk.)
9789811691386 (print)
981169138X
Published
Singapore : Springer, 2022.
Language
English
Description
1 online resource (viii, 88 pages) : illustrations (some color).
Item Number
10.1007/978-981-16-9139-3 doi
Call Number
Q325.5
Dewey Decimal Classification
006.3/1
Summary
This book provides a thorough overview of the evolution of privacy-preserving machine learning schemes over the last ten years, after discussing the importance of privacy-preserving techniques. In response to the diversity of Internet services, data services based on machine learning are now available for various applications, including risk assessment and image recognition. In light of open access to datasets and not fully trusted environments, machine learning-based applications face enormous security and privacy risks. In turn, it presents studies conducted to address privacy issues and a series of proposed solutions for ensuring privacy protection in machine learning tasks involving multiple parties. In closing, the book reviews state-of-the-art privacy-preserving techniques and examines the security threats they face.
Bibliography, etc. Note
Includes bibliographical references.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed March 16, 2022).
Series
SpringerBriefs on cyber security systems and networks, 2522-557X
Available in Other Form
Print version: 9789811691386
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Table of Contents
Introduction
Secure Cooperative Learning in Early Years
Outsourced Computation for Learning
Secure Distributed Learning
Learning with Differential Privacy
Applications - Privacy-Preserving Image Processing
Threats in Open Environment
Conclusion.
Secure Cooperative Learning in Early Years
Outsourced Computation for Learning
Secure Distributed Learning
Learning with Differential Privacy
Applications - Privacy-Preserving Image Processing
Threats in Open Environment
Conclusion.