Privacy-Preserving Machine Learning (Paperback, 1st ed. 2022)

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.

General

Imprint:	Springer Verlag, Singapore
Country of origin:	Singapore
Series:	SpringerBriefs on Cyber Security Systems and Networks
Release date:	March 2022
First published:	2022
Authors:	Jin Li • Ping Li • Zheli Liu • Xiaofeng Chen • Tong Li
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	88
Edition:	1st ed. 2022
ISBN-13:	978-981-16-9138-6
Categories:	Books > Computing & IT > Social & legal aspects of computing > Privacy & data protection Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN:	981-16-9138-X
Barcode:	9789811691386

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