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AHSecAgg and TSKG: Lightweight Secure Aggregation for Federated Learning Without Compromise by Zhang, Siqing; Liao, Yong; Zhou, Pengyuan is a scholarly article available to read on EtoBox.

What is AHSecAgg and TSKG: Lightweight Secure Aggregation for Federated Learning Without Compromise about?

Leveraging federated learning (FL) to enable cross-domain privacy-sensitive data mining represents a vital breakthrough to accomplish privacy-preserving learning. However, attackers can infer the original user data by analyzing the uploaded intermediate parameters during the aggregation process. Therefore, secure aggregation has become a critical issue in the field of FL. Many secure aggregation protocols face the problem of high computation costs, which severely limits their applicability. To this end, we propose AHSecAgg, a lightweight secure aggregation protocol using additive homomorphic masks. AHSecAgg significantly reduces computation overhead without compromising the dropout handling capability or model accuracy. We prove the security of AHSecAgg in semi-honest and active adversary settings. In addition, in cross-silo scenarios where the group of participants is relatively fixed during each round, we propose TSKG, a lightweight Threshold Signature based masking key generation method. TSKG can generate different temporary secrets and shares for different aggregation rounds using the initial key and thus effectively eliminates the cost of secret sharing and key agreement. We p

Author

Zhang, Siqing; Liao, Yong; Zhou, Pengyuan

Published

2023

Language

EN