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Privacy-preserving machine learning

Li, Jin.

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Privacy-preserving machine learning

Record Type:	Electronic resources : Monograph/item
Title/Author:	Privacy-preserving machine learning/ by Jin Li ... [et al.].
other author:	Li, Jin.
Published:	Singapore :Springer Singapore : : 2022.,
Description:	viii, 88 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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
Subject:	Machine learning - Security measures. -
Online resource:	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

Privacy-preserving machine learning
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520 $a 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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