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

A dynamical approach to privacy preserving average consensus