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Privacy-preserving machine learning

Li, Jin.

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Privacy-preserving machine learning

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Privacy-preserving machine learning/ by Jin Li ... [et al.].
其他作者:	Li, Jin.
出版者:	Singapore :Springer Singapore : : 2022.,
面頁冊數:	viii, 88 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Machine learning - Security measures. -
電子資源:	https://doi.org/10.1007/978-981-16-9139-3
ISBN:	9789811691393

Privacy-preserving machine learning
Privacy-preserving machine learning[electronic resource] /by Jin Li ... [et al.]. - Singapore :Springer Singapore :2022. - viii, 88 p. :ill. (some col.), digital ;24 cm. - 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.

ISBN: 9789811691393

Standard No.: 10.1007/978-981-16-9139-3doiSubjects--Topical Terms:

3500846
Machine learning
--Security measures.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

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