



01169nas a2200133   4500008004100000245009300041210006900134260001500203520072300218100001900941700001900960700001900979856003700998       2018                        eng d00aInformation-Theoretic Privacy For Distributed Average Consensus: Bounded Integral Inputs0 aInformationTheoretic Privacy For Distributed Average Consensus B  c03/28/20193 a<p>We propose an asynchronous distributed average consensus algorithm that guarantees information-theoretic privacy of honest agents&#39; 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&#39; 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&#39; inputs are bounded integers, where the bounds are apriori known to all the agents.</p>1 aGupta, Nirupam1 aKatz, Jonathan1 aChopra, Nikhil  uhttps://arxiv.org/abs/1809.01794