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.
History
Chapter number
8
Pagination
189-200
ISBN-13
9781785618734
Language
eng
Publication classification
B1 Book chapter
Extent
12
Editor/Contributor(s)
Huang H, Wang L, Wu Y, Choo K-KR
Publisher
Institution of Engineering & Technology
Place of publication
London, Eng.
Title of book
Blockchains for network security: Principles, technologies and applications