Efficient and privacy-preserving group signature for federated learning-Reference-Cited by-同舟云学术

Efficient and privacy-preserving group signature for federated learning

Published:2023-10 Issue: Volume:147 Page:93-106
ISSN:0167-739X
Container-title:Future Generation Computer Systems
language:en
Short-container-title:Future Generation Computer Systems

Author:

Kanchan Sneha^ORCID,Jang Jae Won^ORCID,Yoon Jun Yong^ORCID,Choi Bong Jun^ORCID

Funder

Institute for Information Communication Technology Planning and Evaluation

Ministry of Science, ICT and Future Planning

National Research Foundation of Korea

Iran Telecommunication Research Center

Publisher

Elsevier BV

Subject

Computer Networks and Communications,Hardware and Architecture,Software

Reference32 articles.

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3. Secure and privacy-aware blockchain design: Requirements, challenges and solutions;Aslam;J. Cybersecur. Priv.,2021

4. Keith Bonawitz, et al., Practical secure aggregation for privacy-preserving machine learning, in: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, Dallas, Texas, USA, 2017, pp. 1175–1191.

5. Privacy-preserving federated learning framework based on chained secure multiparty computing;Li;IEEE Internet Things J.,2021

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