# Cryptography-Based Privacy-Preserving Method for Distributed Optimization over Time-Varying Directed Graphs with Enhanced Efficiency

arXiv: https://arxiv.org/abs/2405.08518 · HTML: https://mlsanity.com/paper/2405.08518/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2024.

## Built on by (most cited first, 1 of 1)

- [A Weighted Gradient Tracking Privacy-Preserving Method for Distributed Optimization](https://mlsanity.com/paper/2509.18134/) (2025)

## Abstract (arXiv)

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.