Privacy-Preserving Distributed Multi-Agent Cooperative Optimization—Paradigm Design and Privacy Analysis
Xiang Huo, Mingxi Liu
Abstract
Large-scale multi-agent cooperative control problems have materially enjoyed the scalability, adaptivity, and flexibility of distributed optimization. However, due to the mandatory iterative communications between the agents and the system operator, the distributed architecture is vulnerable to malicious attacks and privacy breaches. Current research on privacy preservation of both agents and the system operator in cooperative distributed optimization with strongly coupled objective functions and constraints is still primitive. To fill in the gaps, this letter proposes a novel privacy-preserving distributed optimization paradigm based on Paillier cryptosystem. The proposed paradigm achieves ideal correctness and security, as well as resists attacks from a range of adversaries. The efficacy and efficiency of the proposed approach are verified via numerical simulations and a real-world physical platform.