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.

Attached Files
Name Description MIMEType Size Downloads

Title Blockchain-driven privacy-preserving machine learning
Author(s) Qu, YouyangORCID iD for Qu, Youyang orcid.org/0000-0002-2944-4647
Gao, LongxiangORCID iD for Gao, Longxiang orcid.org/0000-0002-3026-7537
Xiang, YongORCID iD for Xiang, Yong orcid.org/0000-0003-3545-7863
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
Persistent URL http://hdl.handle.net/10536/DRO/DU:30155523

Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 14 Abstract Views, 4 File Downloads  -  Detailed Statistics
Created: Fri, 17 Sep 2021, 08:10:52 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.