A dynamical approach to privacy preserving average consensus

TL;DR

This paper introduces a novel continuous-time multiagent consensus method that preserves individual initial states' privacy by using time-varying output masks, ensuring convergence to the average without revealing initial states.

Contribution

It proposes a new privacy-preserving consensus approach using asymptotically converging output masks that prevent disclosure of initial states while achieving consensus.

Findings

Masked system converges to the average consensus value.

Output masks are local, deterministic, and time-varying.

Privacy is preserved as the masked system's equilibrium differs from the unmasked system.

Abstract

In this paper we propose a novel method for achieving average consensus in a continuous-time multiagent network while avoiding to disclose the initial states of the individual agents. In order to achieve privacy protection of the state variables, we introduce maps, called output masks, which alter the value of the states before transmitting them. These output masks are local (i.e., implemented independently by each agent), deterministic, time-varying and converging asymptotically to the true state. The resulting masked system is also time-varying and has the original (unmasked) system as its limit system. It is shown in the paper that the masked system has the original average consensus value as a global attractor. However, in order to preserve privacy, it cannot share an equilibrium point with the unmasked system, meaning that in the masked system the global attractor cannot be also stable.