  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.
