Privacy-Preserving Machine Learning Paperback / softback
by Jin Li, Ping Li, Zheli Liu, Xiaofeng Chen, Tong Li
Part of the SpringerBriefs on Cyber Security Systems and Networks series
Paperback / softback
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Description
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.
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Available to Order - This title is available to order, with delivery expected within 2 weeks
- Format:Paperback / softback
- Pages:88 pages, 18 Illustrations, color; 3 Illustrations, black and white; VIII, 88 p. 21 illus., 18 illus
- Publisher:Springer Verlag, Singapore
- Publication Date:15/03/2022
- Category:
- ISBN:9789811691386
Other Formats
- EPUB from £42.49
Information
-
Available to Order - This title is available to order, with delivery expected within 2 weeks
- Format:Paperback / softback
- Pages:88 pages, 18 Illustrations, color; 3 Illustrations, black and white; VIII, 88 p. 21 illus., 18 illus
- Publisher:Springer Verlag, Singapore
- Publication Date:15/03/2022
- Category:
- ISBN:9789811691386
