Federated Learning (FL) preserves client data privacy by distributing model training but remains vulnerable to inference attacks (e.g., gradient inversion). Existing secure aggregation schemes mitigate basic privacy threats, but most of them are under the semi-honest server assumption. Malicious servers can corrupt the global model through forging aggregation results. Moreover, the high interaction rounds and communication complexity of the existing schemes still constrain their feasibility in large-scale distributed deployment scenarios. To tackle these challenges, we propose EvaFL, an efficient verifiable privacy-preserving federated learning against malicious servers, which reduces the communication overhead and privacy threats from malicious severs. We propose the system model of EvaFL and give the concrete protocol. We leverage the linear homomorphism property of Shamir secret sharing under discrete logarithm assumption to reuse the mask seed shares, which avoids the communication overhead caused by share distribution in multiple rounds of iterations. In addition, by integrating consistency checking into the unmasking step, we further reduce one round interaction. To resist malicious servers, we adopt linear homomorphic hash to realize the correctness verification of the aggregation results. Finally, we implement and evaluate our EvaFL based on MNIST and CIFAR10 datasets to show its feasibility for privacy training. The single round aggregation completion time of EvaFL is reduced by 69% compared to BBGLR (CCS 2020) and by 11% compared to Flamingo (S&P 2023).

Public auditing schemes for cloud storage systems have been extensively explored with the increasing importance of data integrity. A third-party auditor (TPA) is introduced in public auditing schemes to verify the integrity of outsourced data on behalf of users. To resist malicious TPAs, many blockchain-based public verification schemes have been proposed. However, existing auditing schemes rely on a centralized TPA, and they are vulnerable to tempting auditors who may collude with malicious blockchain miners to produce biased auditing results. In this article, we propose a blockchain-based decentralized public auditing (BDPA) scheme by utilizing a decentralized blockchain network to undertake the responsibility of a centralized TPA, and also mitigate the influence of tempting auditors and malicious blockchain miners by taking the concept of decentralized autonomous organization (DAO). A detailed security analysis shows that BDPA can preserve data integrity against tempting auditors and malicious blockchain miners. A comprehensive performance evaluation demonstrates that BDPA is feasible and scalable.