Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Privacy-Preserving Machine Learning

Privacy-Preserving Machine Learning

Li, Jin
Li, Ping
Liu, Zheli

Paperback

Series: Springerbriefs on Cyber Security Systems and Networks

General ComputersProbability & StatisticsComputer Security

ISBN10: 981169138X
ISBN13: 9789811691386
Publisher: Springer
Published: Mar 15 2022
Pages: 88
Weight: 0.33
Height: 0.20 Width: 6.14 Depth: 9.21
Language: English

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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