Cryptography-Based Privacy-Preserving Method for Distributed Optimization Over Time-Varying Directed Graphs With Enhanced Efficiency

In this article, we study the privacy-preserving distributed optimization problem, aiming to prevent attackers from stealing the private information of agents. For this purpose, we propose a novel privacy-preserving algorithm based on the Advanced Encryption Standard, which is both secure and computationally efficient. By appropriately constructing the underlying weight matrices, our algorithm can be applied to time-varying directed networks. We show that the proposed algorithm can protect an agent's privacy if the agent has at least one honest neighbor at the initial iteration. Under the assumption that the objective function is strongly convex and Lipschitz smooth, we rigorously prove that the proposed algorithm has a linear convergence rate. Finally, the effectiveness of the proposed algorithm is demonstrated by numerical simulations of the canonical sensor fusion problem.