同态加密
计算机科学
对手
加密
数学证明
方案(数学)
利用
嵌入
理论计算机科学
分布式计算
计算机网络
计算机安全
数学
人工智能
数学分析
几何学
作者
Haibo Tian,Yanchuan Wen,Fangguo Zhang,Yunfeng Shao,Bingshuai Li
标识
DOI:10.1016/j.csi.2023.103765
摘要
In federated learning (FL), a parameter server needs to aggregate user gradients and a user needs to protect the value of their gradients. Among all the possible solutions to the problem, those based on additive homomorphic encryption (AHE) are natural. As users may drop out in FL and an adversary could corrupt some users and the parameter server, we require a dropout-resilient AHE scheme with a distributed key generation algorithm. In this paper, we aim to provide a lattice based distributed threshold AHE (DTAHE) scheme and to show their applications in FL. The main merit of the DTAHE scheme is to save communication bandwidth compared with other latticed based DTAHE schemes. Embedding the scheme into FL, we get two secure aggregation protocols. One is secure against a semi-honest adversary and the other is secure against an active adversary. The latter exploits a smart contract in a ledger. Finally, we provide security proofs and performance analysis for the scheme and protocols.
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