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 prove TSKG does not sacrifice security. Extensive experiments show that AHSecAgg significantly outperforms state-of-the-art mask-based secure aggregation protocols in terms of computational efficiency, and TSKG effectively reduces the computation and communication costs for existing secure aggregation protocols.

翻译：利用联邦学习实现跨领域隐私敏感数据挖掘是达成隐私保护学习的重要突破。然而，攻击者可通过分析聚合过程中上传的中间参数推断原始用户数据，因此安全聚合已成为联邦学习领域的核心问题。现有许多安全聚合协议面临计算成本高昂的问题，严重限制了其适用性。为此，我们提出AHSecAgg——一种基于加法同态掩码的轻量级安全聚合协议。AHSecAgg在保持丢包容错能力与模型精度的前提下显著降低了计算开销。我们在半诚实与主动敌手模型下证明了AHSecAgg的安全性。此外，针对参与组群在每轮聚合中相对固定的跨孤岛场景，我们提出TSKG——一种基于门限签名的轻量级掩码密钥生成方法。TSKG可利用初始密钥为不同聚合轮次生成不同的临时秘密值与份额，从而有效消除秘密共享与密钥协商的开销。我们证明TSKG未牺牲安全性。大量实验表明，AHSecAgg在计算效率上显著优于当前最先进的基于掩码的安全聚合协议，而TSKG有效降低了现有安全聚合协议的计算与通信成本。