Privacy-Preserving Machine Learning - Tong Li,Ping Li,Zheli Liu,Jin Li,Xiaofeng Chen
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Saadetis 12-18 tööpäeva jooksul
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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 datas ... Täielik kirjeldus
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Kirjeldus
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.
Lisateave
| Autor | Tong Li, Ping Li, Zheli Liu, Jin Li, Xiaofeng Chen |
|---|---|
| Kirjastaja | Springer Nature Singapore |
| Series | SpringerBriefs on Cyber Security Systems and Networks |
| Väljalaskeaasta | 2022 |
| Kaanetüüp | Pehme kaanega |
| EAN | 9789811691386 |