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Average consensus is key for distributed networks, with applications rangingfrom network synchronization, distributed information fusion, decentralizedcontrol, to load balancing for parallel processors. Existing average consensusalgorithms require each node to exchange explicit state values with itsneighbors, which results in the undesirable disclosure of sensitive stateinformation. In this paper, we propose a novel average consensus approach fordirected graphs which can protect the privacy of participating nodes' initialstates without the assistance of any trusted third party or data aggregator. Byleveraging the inherent robustness of consensus dynamics to embed privacy inrandom coupling weights between interacting nodes, our proposed approach canguarantee consensus to the exact value without any error. This is in distinctdifference from differential-privacy based average consensus approaches whichenable privacy through sacrificing accuracy in obtained consensus value. Theproposed approach is able to preserve privacy even when multiplehonest-but-curious nodes collude with each other. Furthermore, by encryptingexchanged information, the proposed approach can also provide privacyprotection against inference by external eavesdroppers wiretappingcommunication links. Numerical simulations and hardware experiments onRaspberry Pi boards confirm that the algorithm is lightweight in computationand communication.

Dynamics Based Privacy Protection for Average Consensus on Directed Graphs