发表机构
School of Electrical and Computer Engineering at the Georgia Institute of Technology（佐治亚理工学院电气与计算机工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文研究随机线性二次动态博弈中差分隐私与性能的权衡，推导隐私代价解析式，证明目标一致时噪声必然损害性能，而目标不一致时噪声可同时保护隐私并改善均衡性能。
AI 中文摘要
多智能体协调通常需要策略性智能体共享关于其状态或目标的敏感信息，这造成了性能与隐私之间的张力。我们的论文在具有异构智能体目标的随机线性二次（LQ）动态博弈中研究这种权衡。在我们的框架中，智能体与一个云计算机共享受噪声扰动的状态和参考信息，该云计算机计算反馈纳什均衡策略，注入的噪声被校准以提供差分隐私。我们推导了每个智能体相对于非隐私博弈的无限时域稳态隐私代价的解析表达式。然后，我们证明当智能体的目标充分一致时，隐私噪声的注入必然会产生正的性能代价。相反，我们刻画了一类智能体间目标充分不一致的博弈，其中智能体的隐私代价可以严格为负。因此，与直觉相反，当交互智能体的目标充分不一致时，噪声可以同时保护隐私并改善智能体的均衡性能。最后，我们提供了数值实验，以证实我们的理论贡献。
英文摘要
Multi-agent coordination often requires strategic agents to share sensitive information about their states or objectives, creating a tension between performance and privacy. Our paper studies this tradeoff in stochastic linear-quadratic (LQ) dynamic games with heterogeneous agent objectives. In our framework, agents share noise-perturbed state and reference information with a cloud computer that computes feedback Nash equilibrium strategies, with the injected noise calibrated to provide differential privacy. We derive an analytical expression for each agent's infinite-horizon steady-state cost of privacy relative to the non-private game. Then, we prove that when agents' objectives are sufficiently aligned, the injection of privacy noise necessarily incurs a positive performance cost. In contrast, we characterize a class of games with sufficiently misaligned objectives across agents for which an agent's cost of privacy can be strictly negative. Thus, counterintuitively, noise can simultaneously protect privacy and improve the equilibrium performance of an agent when the objectives of interacting agents are sufficiently misaligned. Finally, we present numerical experiments which corroborate our theoretical contributions.
Comments10 pages, 2 figures
