Privacy-Preserving Distributed Nash Equilibrium Seeking for Noncooperative Games With Masked Interactive Information

Abstract

The privacy preservation problem in distributed Nash equilibrium (NE) seeking for noncooperative games is studied in this article. Different from adding random noises to the transmitted data in estimators for some global information, a dynamical privacy preservation method is used to both protect each agent's strategy and avoid reconstructing the privacy of agents. Thus, a novel distributed algorithm is proposed with the masked interactive information to seek NE. Moreover, a discrete-time communication scheme is designed based on an event-triggering rule without the Zeno behavior for the multiagent noncooperative game. The accurate convergence of the designed distributed algorithm is established, rather than the bounded convergence of the differential privacy-based algorithms. The performance of the designed algorithm with communication delay is also analyzed. Cournot competition with distributed energy resources is taken as an example to verify the proposed algorithm.