论文标题
确保通过耦合约束的共识跟踪和聚合游戏中的差异隐私和收敛精度
Ensure Differential Privacy and Convergence Accuracy in Consensus Tracking and Aggregative Games with Coupling Constraints
论文作者
论文摘要
我们解决具有共享耦合约束的完全分布的聚合游戏的差异隐私。通过共同设计广义的NASH平衡（GNE）寻求机制和差异私人噪声注入机制，我们提出了第一个寻求GNE寻求算法的GNE，这些算法可以确保与GNE和严格的Epsilon-differential Privacy均可证明的融合，即使触发无限的迭代次数。作为共同设计的基础，我们还提出了一种新的共识跟踪算法，该算法可以实现严格的Epsilon-Differential隐私，同时保持准确的跟踪性能，据我们所知，以前尚未实现。为了促进收敛分析，我们还为随机扰动的非平稳固定点迭代过程建立了一般的收敛结果，这是许多优化和变分问题的核心。数值模拟结果证实了所提出的方法的有效性。
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 new 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.
