Privacy-Preserving Distributed Multi-Agent Cooperative Optimization—Paradigm Design and Privacy Analysis

作者：Xiang Huo, Mingxi Liu · 发表于：IEEE Control Systems Letters · 年份：2021 · DOI：10.1109/lcsys.2021.3086441 · 被引用次数：23 · 研究领域：Privacy-Preserving Technologies in Data、Cryptography and Data Security、Stochastic Gradient Optimization Techniques

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.