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portada Privacy-Preserving Machine Learning
Type
Physical Book
Publisher
Language
English
Pages
88
Format
Paperback
Dimensions
23.4 x 15.6 x 0.5 cm
Weight
0.15 kg.
ISBN13
9789811691386

Privacy-Preserving Machine Learning

Ping Li (Author) · Jin Li (Author) · Zheli Liu (Author) · Springer · Paperback

Privacy-Preserving Machine Learning - Li, Jin ; Li, Ping ; Liu, Zheli

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Synopsis "Privacy-Preserving Machine Learning"

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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