Overview
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.
The book, Privacy-Preserving Machine Learning (SpringerBriefs on Cyber Security Systems and Networks) [Bulk, Wholesale, Quantity] ISBN#9789811691386 in Paperback by Jin Li, Ping Li, Zheli Liu, Xiaofeng Chen, Tong Li may be ordered in bulk quantities. Minimum starts at 25 copies. Availability based on publisher status and quantity being ordered.
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