Anonymous Authentication Scheme for Federated Learning

计算机科学 认证(法律) 匿名 协议(科学) 方案(数学) 计算机安全 群签名 密码学 联合学习 身份验证协议 信息隐私 密码协议 公钥密码术 分布式计算 加密 病理 数学分析 替代医学 医学 数学
作者
Tianqi Zhou,Jian Shen,Pandi Vijayakumar,Md Zakirul Alam Bhuiyan,S. Audithan
标识
DOI:10.1109/infocomwkshps57453.2023.10225800
摘要

As a distributed machine learning model, federated learning ensures the legitimate use of data and user privacy while training the global model. Existing privacy protection mechanisms for federated learning either need to balance training accuracy and privacy protection requirements, or lack of design from the perspective of groups. In this paper, we design a privacy protection mechanism from the perspective of groups for federated learning. By resorting to cryptographic techniques, the proposed mechanism is free of the tradeoff between accuracy and privacy. In particular, we aim to develop novel approaches for the asymmetric group key agreement (AGKA) protocol with efficient operations and lower storage cost, as well as to further support anonymous group authentication. First, we propose a BLS-AGKA protocol by using the Boneh-Lynn-Shacham (BLS) signature, which is computationally efficient and requires a relatively small storage cost. Second, to further achieve the privacy-preserving demand in federated learning, we construct an anonymous authentication scheme based on the proposed BLS-AGKA protocol, which supports anonymous group authentication. Finally, it is shown that the proposed protocol and scheme guarantee the desired security properties, including session-key security, unforgeability, and anonymity. In addition, the performance of the proposed scheme is superior to relevant existing works as well.
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