Practical and Collusion-Resistant Privacy-Preserving Aggregation for Edge Intelligence
Guohao Li, Li Yang, Lu Zhou, Hongbin Huang, Hao Zhang, Jianfeng Ma · IEEE Transactions on Dependable and Secure Computing · 2025
Privacy-preserving data aggregation (PDA) enables an edge server to securely perform aggregation tasks on data generated by terminal devices in edge intelligence (EI) systems, revealing only the result without exposing individual inputs. However, most existing solutions, such as homomorphic encryption and federated learning, support only basic functions (e.g., SUM or AVG). They often fail to achieve privacy protection, fault tolerance, and lightweight terminal-side operations when the server colludes with compromised devices. In this work, we propose PrivEI, a practical PDA scheme for EI systems. It uses a proposed collusion-resistant symmetric masking scheme that enables an untrusted edge server to collect and decode masked inputs from$n$terminal devices while supporting arbitrary computations. The scheme allows the server to collude with$k \leq n - 2$terminal devices and has a lightweight mechanism to tolerate device dropouts during aggregation. PrivEI further leverages the Chinese Remainder Theorem to avoid frequent mask updates when aggregating multi-dimensional data, and ensures data integrity using a signer-efficient multiple-time elliptic curve signature algorithm. We formally prove that PrivEI ensures input privacy and achieves$(n-k)$-source anonymity. Both theoretical analysis and experimental results confirm that it offers superior functionality with performance comparable to existing approaches. We have open-sourced the implementation.