  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.
