On Privatizing Equilibrium Computation in Aggregate Games over Networks

TL;DR

This paper introduces a distributed algorithm for computing equilibria in aggregate games over networks that preserves player privacy without sacrificing accuracy, using correlated perturbation instead of differential privacy.

Contribution

The paper presents a novel privacy-preserving distributed equilibrium computation method leveraging correlated perturbation, avoiding accuracy loss typical of differential privacy.

Findings

Algorithm guarantees privacy against honest-but-curious adversaries.

No accuracy loss in equilibrium computation due to privacy measures.

Effective in networked aggregate game settings.

Abstract

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.