An Efficient and Verifiable Data Aggregation Protocol with Enhanced Privacy Protection

Distributed data fusion is essential for numerous applications, yet faces significant privacy security challenges. Federated learning (FL), as a distributed machine learning paradigm, offers enhanced data privacy protection and has attracted widespread attention. Consequently, research increasingly focuses on developing more secure FL techniques. However, in real-world scenarios involving malicious entities, the accuracy of FL results is often compromised, particularly due to the threat of collusion between two servers. To address this challenge, this paper proposes an efficient and verifiable data aggregation protocol with enhanced privacy protection. After analyzing attack methods against prior schemes, we implement key improvements. Specifically, by incorporating cascaded random numbers and perturbation terms into gradients, we strengthen the privacy protection afforded by polynomial masking, effectively preventing information leakage. Furthermore, our protocol features an enhanced verification mechanism capable of detecting collusive behaviors between two servers. Accuracy testing on the MNIST and CIFAR-10 datasets demonstrates that our protocol maintains accuracy comparable to the Federated Averaging Algorithm. In scheme efficiency comparisons, while incurring only a marginal increase in verification overhead relative to the baseline scheme, our protocol achieves an average improvement of 93.13% in privacy protection and verification overhead compared to the state-of-the-art scheme. This result highlights its optimal balance between overall overhead and functionality. A current limitation is that the verification mechanism cannot precisely pinpoint the source of anomalies within aggregated results when server-side malicious behavior occurs. Addressing this limitation will be a focus of future research.