Blockchain-driven privacy-preserving machine learning

Qu, Youyang, Gao, Longxiang and Xiang, Yong 2020, Blockchain-driven privacy-preserving machine learning. In Huang, H, Wang, L, Wu, Y and Choo, K-KR (ed), Blockchains for network security: Principles, technologies and applications, Institution of Engineering & Technology, London, Eng., pp.189-200, doi: 10.1049/PBPC029E_ch8.

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Title Blockchain-driven privacy-preserving machine learning
Author(s) Qu, YouyangORCID iD for Qu, Youyang
Gao, LongxiangORCID iD for Gao, Longxiang
Xiang, YongORCID iD for Xiang, Yong
Title of book Blockchains for network security: Principles, technologies and applications
Editor(s) Huang, H
Wang, L
Wu, Y
Choo, K-KR
Publication date 2020
Chapter number 8
Total chapters 12
Start page 189
End page 200
Total pages 12
Publisher Institution of Engineering & Technology
Place of Publication London, Eng.
Summary With the integration of blockchain with current leading privacy-preserving machine learning mechanism, the performances of FL and GAN-DP can be further improved, especially the robustness against poisoning attacks. In addition, the deployment of blockchain as the underlying architecture enables decentralization while providing incentive mechanisms. Furthermore, the efficiency can be guaranteed, and the storage resources can be saved with an off-chain structure. Future directions in this field may include the optimization using game theory and reversible blockchain using chameleon hash. Chapter Contents: • 8.1 GAN-DP and blockchain • 8.1.1 Wasserstein generative adversarial net • 8.1.2 Generator and discriminator • 8.1.3 GAN-DP with a DP identifier • 8.1.4 Decentralized privacy • 8.1.5 Further discussion • 8.2 Federated learning and blockchain • 8.2.1 Existing issues • 8.2.2 How blockchain benefits FL • 8.2.3 Blockchain-enabled federated learning • 8.3 Conclusion remarks • References.
ISBN 9781785618734
Language eng
DOI 10.1049/PBPC029E_ch8
HERDC Research category B1 Book chapter
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