Information-Theoretic Privacy For Distributed Average Consensus: Bounded Integral Inputs

We propose an asynchronous distributed average consensus algorithm that guarantees information-theoretic privacy of honest agents' inputs against colluding passive adversarial agents, as long as the set of colluding passive adversarial agents is not a vertex cut in the underlying communication network. This implies that a network with (t+1)-connectivity guarantees information-theoretic privacy of honest agents' inputs against any t colluding agents. The proposed protocol is formed by composing a distributed privacy mechanism we provide with any (non-private) distributed average consensus algorithm. The agent' inputs are bounded integers, where the bounds are apriori known to all the agents.