A Robust Dynamic Average Consensus Algorithm that Ensures both Differential Privacy and Accurate Convergence

TL;DR

This paper introduces a new dynamic average consensus algorithm that guarantees both accurate convergence and differential privacy, even in noisy communication environments, advancing secure distributed computation.

Contribution

The paper presents a novel consensus algorithm that simultaneously ensures convergence accuracy and rigorous differential privacy, addressing a gap in existing methods.

Findings

Ensures convergence to the exact average despite privacy noise.

Provides epsilon-differential privacy guarantees for the algorithm.

Effective in counteracting communication channel noise.

Abstract

We propose a new dynamic average consensus algorithm that is robust to information-sharing noise arising from differential-privacy design. Not only is dynamic average consensus widely used in cooperative control and distributed tracking, it is also a fundamental building block in numerous distributed computation algorithms such as multi-agent optimization and distributed Nash equilibrium seeking. We propose a new dynamic average consensus algorithm that is robust to persistent and independent information-sharing noise added for the purpose of differential-privacy protection. In fact, the algorithm can ensure both provable convergence to the exact average reference signal and rigorous epsilon-differential privacy (even when the number of iterations tends to infinity), which, to our knowledge, has not been achieved before in average consensus algorithms. Given that channel noise in communication can be viewed as a special case of differential-privacy noise, the algorithm can also be used to counteract communication imperfections. Numerical simulation results confirm the effectiveness of the proposed approach.