# On Privatizing Equilibrium Computation in Aggregate Games over Networks

arXiv: https://arxiv.org/abs/1912.06296 · HTML: https://mlsanity.com/paper/1912.06296/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2019.

## Built on by (most cited first, 2 of 2)

- [Privacy-Preserving Distributed Optimization and Learning](https://mlsanity.com/paper/2403.00157/) (2024)
- [Differentially Private Equilibrium Finding in Polymatrix Games](https://mlsanity.com/paper/2503.09538/) (2025)

## Abstract (arXiv)

We propose a distributed algorithm to compute an equilibrium in aggregate games where players communicate over a fixed undirected network. Our algorithm exploits correlated perturbation to obfuscate information shared over the network. We prove that our algorithm does not reveal private information of players to an honest-but-curious adversary who monitors several nodes in the network. In contrast with differential privacy based algorithms, our method does not sacrifice accuracy of equilibrium computation to provide privacy guarantees.