Differentially Private Distributed Algorithms for Aggregative Games With Guaranteed Convergence

The distributed computation of a Nash equilibrium in aggregative games is gaining increased attention in recent years. Of particular interest is the coordinator-free scenario where individual players only observe the decisions of their neighbors due to practical constraints. Given the noncooperative relationship among participating players, protecting the privacy of individual players becomes imperative when sensitive information is involved. We propose a fully distributed equilibrium-seeking approach for aggregative games that can achieve both rigorous differential privacy and guaranteed computation accuracy of the Nash equilibrium. This is in sharp contrast to existing differential-privacy solutions for aggregative games that have to either sacrifice the accuracy of equilibrium computation to gain rigorous privacy guarantees or allow the cumulative privacy budget to grow unbounded, hence, losing privacy guarantees as iteration proceeds. Our approach uses independent noises across players, thus making it effective even when adversaries have access to all shared messages as well as the underlying algorithm structure. The encryption-free nature of the proposed approach also ensures efficiency in computation and communication. The approach is also applicable in stochastic aggregative games, able to ensure both rigorous differential privacy and guaranteed computation accuracy of the Nash equilibrium when individual players only have stochastic estimates of their pseudogradient mappings. Numerical comparisons with existing counterparts confirm the effectiveness of the proposed approach.