File(s) under permanent embargo
Blockchain-driven privacy-preserving machine learning
chapter
posted on 2020-01-01, 00:00 authored by Youyang Qu, Longxiang GaoLongxiang Gao, Yong XiangYong XiangWith 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
Title of book
Blockchains for network security: Principles, technologies and applicationsChapter number
8Pagination
189 - 200Publisher
Institution of Engineering & TechnologyPlace of publication
London, Eng.Publisher DOI
ISBN-13
9781785618734Language
engPublication classification
B1 Book chapterExtent
12Editor/Contributor(s)
Haojun Huang, Lizhe Wang, Yulei Wu, Kim-Kwang ChooUsage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC