Privacy-Preserving Dynamic Average Consensus by Masking Reference Signals

TL;DR

This paper introduces a privacy-preserving dynamic average consensus algorithm that masks reference signals using random numbers and encryption, maintaining convergence accuracy while protecting privacy in multi-agent systems.

Contribution

It proposes a novel masking technique for DAC that ensures privacy without sacrificing convergence speed or accuracy.

Findings

The method achieves the same convergence rate as traditional DAC.

It effectively protects reference signals from eavesdroppers and curious agents.

Numerical simulations confirm the algorithm's effectiveness.

Abstract

In multi-agent systems, dynamic average consensus (DAC) is a decentralized estimation strategy in which a set of agents tracks the average of time-varying reference signals. Because DAC requires exchanging state information with neighbors, attackers may gain access to these states and infer private information. In this paper, we develop a privacy-preserving method that protects each agent's reference signal from external eavesdroppers and honest-but-curious agents while achieving the same convergence accuracy and convergence rate as conventional DAC. Our approach masks the reference signals by having each agent draw a random real number for each neighbor, exchanges that number over an encrypted channel at the initialization, and computes a masking value to form a masked reference. Then the agents run the conventional DAC algorithm using the masked references. Convergence and privacy analyses show that the proposed algorithm matches the convergence properties of conventional DAC while preserving the privacy of the reference signals. Numerical simulations validate the effectiveness of the proposed privacy-preserving DAC algorithm.