Differentially-Private Distributed Optimization with Guaranteed Optimality

Privacy protection is gaining increased attention in distributed optimization and learning. As differential privacy is becoming a de facto standard for privacy preservation, recently results have emerged integrating differential privacy with distributed optimization. However, to ensure differential privacy (with a finite cumulative privacy budget), all existing approaches have to sacrifice provable convergence to the optimal solution. In this paper, we propose a differentially-private distributed optimization algorithm that can ensure, for the first time, both ϵ-differential privacy and optimality, even on the infinite time horizon. Numerical simulation results confirm the effectiveness of the proposed approach.