  Average consensus is key for distributed networks, with applications ranging
from network synchronization, distributed information fusion, decentralized
control, to load balancing for parallel processors. Existing average consensus
algorithms require each node to exchange explicit state values with its
neighbors, which results in the undesirable disclosure of sensitive state
information. In this paper, we propose a novel average consensus approach for
directed graphs which can protect the privacy of participating nodes' initial
states without the assistance of any trusted third party or data aggregator. By
leveraging the inherent robustness of consensus dynamics to embed privacy in
random coupling weights between interacting nodes, our proposed approach can
guarantee consensus to the exact value without any error. This is in distinct
difference from differential-privacy based average consensus approaches which
enable privacy through sacrificing accuracy in obtained consensus value. The
proposed approach is able to preserve privacy even when multiple
honest-but-curious nodes collude with each other. Furthermore, by encrypting
exchanged information, the proposed approach can also provide privacy
protection against inference by external eavesdroppers wiretapping
communication links. Numerical simulations and hardware experiments on
Raspberry Pi boards confirm that the algorithm is lightweight in computation
and communication.
