Statistical Privacy-Preserving Online Distributed Nash Equilibrium Tracking in Aggregative Games

• 成果类型: Article
• 署名作者: Lin, Yeming; Liu, Kun; Han, Dongyu; Xia, Yuanqing
• 署名单位: Beijing Institute of Technology
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2023.3264164
• 发表日期: 2024
• 页码: 323-330
• 关键词: privacy; games; Nash equilibrium; cost function; Aggregates; Heuristic algorithms; Perturbation methods; Aggregative game; distributed online algorithm; privacy preservation

摘要

This article considers an online aggregative game equilibrium problem subject to privacy preservation, where all players aim at tracking the time-varying Nash equilibrium, while some players are corrupted by an adversary. We propose a distributed online Nash equilibrium tracking algorithm, where a correlated perturbation mechanism is employed to mask the local information of the players. Our theoretical analysis shows that the proposed algorithm can achieve a sublinear expected regret bound while preserving the privacy of uncorrupted players. We use the Kullback-Leibler divergence to analyze the privacy bound in a statistical sense. Furthermore, we present a tradeoff between the expected regret and the statistical privacy, to obtain a constant privacy bound when the regret bound is sublinear.