Privacy-Preserving Machine Learning
Li, Jin.
Privacy-Preserving Machine Learning [electronic resource] / by Jin Li, Ping Li, Zheli Liu, Xiaofeng Chen, Tong Li. - 1st ed. 2022. - VIII, 88 p. 21 illus., 18 illus. in color. online resource. - SpringerBriefs on Cyber Security Systems and Networks, 2522-557X . - SpringerBriefs on Cyber Security Systems and Networks, .
Introduction -- Secure Cooperative Learning in Early Years -- Outsourced Computation for Learning -- Secure Distributed Learning -- Learning with Differential Privacy -- Applications - Privacy-Preserving Image Processing -- Threats in Open Environment -- Conclusion.
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.
9789811691393
10.1007/978-981-16-9139-3 doi
Data protection--Law and legislation.
Machine learning.
Privacy.
Machine Learning.
QA76.9.A25 JC596-596.2
005.8 323.448
Privacy-Preserving Machine Learning [electronic resource] / by Jin Li, Ping Li, Zheli Liu, Xiaofeng Chen, Tong Li. - 1st ed. 2022. - VIII, 88 p. 21 illus., 18 illus. in color. online resource. - SpringerBriefs on Cyber Security Systems and Networks, 2522-557X . - SpringerBriefs on Cyber Security Systems and Networks, .
Introduction -- Secure Cooperative Learning in Early Years -- Outsourced Computation for Learning -- Secure Distributed Learning -- Learning with Differential Privacy -- Applications - Privacy-Preserving Image Processing -- Threats in Open Environment -- Conclusion.
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.
9789811691393
10.1007/978-981-16-9139-3 doi
Data protection--Law and legislation.
Machine learning.
Privacy.
Machine Learning.
QA76.9.A25 JC596-596.2
005.8 323.448