We address differential privacy for fully distributed aggregative games with shared coupling constraints. By co-designing the generalized Nash equilibrium (GNE) seeking mechanism and the differential-privacy noise injection mechanism, we propose the first GNE seeking algorithm that can ensure both provable convergence to the GNE and rigorous epsilon-differential privacy, even with the number of iterations tending to infinity. As a basis of the co-design, we also propose a differentially private consensus-tracking algorithm that can achieve rigorous epsilon-differential privacy while maintaining accurate tracking performance, which, to our knowledge, has not been achieved before. To facilitate the convergence analysis, we also establish a general convergence result for stochastically-perturbed nonstationary fixed-point iteration processes, which lie at the core of numerous optimization and variational problems. Numerical simulation results confirm the effectiveness of the proposed approach.

翻译：通过共同设计普遍纳什平衡(GNE)寻求的机制和不同隐私的噪音注射机制,我们建议采用第一个通用纳什平衡(GNE)寻求的算法,确保与GNE的可证实的趋同和严格的普西隆差异性隐私,即使迭代次数往往具有无限性。作为共同设计的基础,我们还建议采用差别化的私人共识跟踪算法,既能实现严格的普西伦差异性隐私,又能保持准确的跟踪性能,据我们所知,这是以前没有实现的。为了促进趋同性分析,我们还为处于许多优化和变异性问题的核心的随机性非静态固定点循环过程确立了总体趋同结果。