000			01983cam a2200265Ii 4500
020			_a9789811691386
020			_a9789811691386
082	0	4	_aQ325.5 .P56 2022 _b3
100	1		_aLi, Jin,
245	1	0	_aPrivacy-preserving machine learning / _cJin Li, Ping Li, Zheli Liu, Xiaofeng Chen, Tong Li
264		1	_aSingapore : _bSpringer, _c2022
300			_aviii, 88 pages _billustrations
490	1		_aSpringerBriefs on cyber security systems and networks,
505	0		_aIntroduction -- 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
520			_aThis 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
650		0	_aMachine learning
700	1		_aLi, Ping,
700	1		_aLiu, Zheli,
700	1		_aChen, Xiaofeng,
700	1		_aLi, Tong,
856	4	0	_uhttps://rave.ohiolink.edu/ebooks/ebc2/9789811691393
856	4	0	_uhttps://link.springer.com/10.1007/978-981-16-9139-3
856	4	0	_uhttps://go.ohiolink.edu/goto?url=https://link.springer.com/10.1007/978-981-16-9139-3
942			_2lcc _cBK
999			_c13900 _d13900