论文标题
确保在纳什均衡中寻求纳什均衡的可证明的融合和差异隐私
Ensuring both Provable Convergence and Differential Privacy in Nash Equilibrium Seeking on Directed Graphs
论文作者
论文摘要
我们在本文中研究了完全分布的NASH平衡寻求隐私保护，从而在播放器中只能通过定向通信网络从其直接邻居那里接收信息。鉴于基本决策过程的不合作性质，必须在涉及敏感信息时保护单个玩家在网络游戏中的隐私。我们提出了一种方法，可以在分布式NASH均衡寻求方面获得准确的融合和严格的差异隐私，并具有有限的累积隐私预算，这与现有的差异化游戏的现有差异性私人关系解决方案形成鲜明对比，这些解决方案必须用于差异隐私的交易融合准确性。即使通信图不平衡，该方法也适用，并且不需要单个玩家具有通信图的任何全局结构信息。由于该方法利用独立的噪声来保护隐私保护，因此可以打击具有网络中所有共享消息的对手。它也是无加密的，可确保沟通和计算的高效率。与现有对应物的数值比较结果证实了拟议方法的有效性。
We study in this paper privacy protection in fully distributed Nash equilibrium seeking where a player can only access its own cost function and receive information from its immediate neighbors over a directed communication network. In view of the non-cooperative nature of the underlying decision-making process, it is imperative to protect the privacy of individual players in networked games when sensitive information is involved. We propose an approach that can achieve both accurate convergence and rigorous differential privacy with finite cumulative privacy budget in distributed Nash equilibrium seeking, which is in sharp contrast to existing differential-privacy solutions for networked games that have to trade convergence accuracy for differential privacy. The approach is applicable even when the communication graph is unbalanced and it does not require individual players to have any global structure information of the communication graph. Since the approach utilizes independent noises for privacy protection, it can combat adversaries having access to all shared messages in the network. It is also encryption-free, ensuring high efficiency in communication and computation. Numerical comparison results with existing counterparts confirm the effectiveness of the proposed approach.
