  In this paper, 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 (AES), 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 legitimate 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.
