Description: Privacy-preserving Machine Learning, Paperback by Li, Jin; Li, Ping; Liu, Zheli; Chen, Xiaofeng; Li, Tong, ISBN 981169138X, ISBN-13 9789811691386, Brand New, Free shipping in the US
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, th reviews state-of-the-art privacy-preserving techniques and examines the security threats they face.
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Book Title: Privacy-preserving Machine Learning
Item Length: 9.3in
Item Width: 6.1in
Author: Xiaofeng Chen, Ping Li, Tong Li, Zheli Liu, Jin Li
Publication Name: Privacy-Preserving Machine Learning
Format: Trade Paperback
Language: English
Publisher: Springer
Publication Year: 2022
Series: Springerbriefs ON Cyber Security Systems and Networks Ser.
Type: Textbook
Item Weight: 5.7 Oz
Number of Pages: VIII, 88 Pages
