This paper proposes a privacy protocol for distributed average consensus algorithms on bounded real-valued inputs that guarantees statistical privacy of honest agents' inputs against colluding (passive adversarial) agents, if the set of colluding agents is not a vertex cut in the underlying communication network. This implies that privacy of agents' inputs is preserved against t number of arbitrary colluding agents if the connectivity of the communication network is at least (t+1). A similar privacy protocol has been proposed for the case of bounded integral inputs in our previous paper~\cite{gupta2018information}. However, many applications of distributed consensus concerning distributed control or state estimation deal with real-valued inputs. Thus, in this paper we propose an extension of the privacy protocol in~\cite{gupta2018information}, for bounded real-valued agents' inputs, where bounds are known apriori to all the agents.

1 aGupta, Nirupam1 aKatz, Jonathan1 aChopra, Nikhil uhttps://arxiv.org/abs/1903.0931501169nas a2200133 4500008004100000245009300041210006900134260001500203520072300218100001900941700001900960700001900979856003700998 2018 eng d00aInformation-Theoretic Privacy For Distributed Average Consensus: Bounded Integral Inputs0 aInformationTheoretic Privacy For Distributed Average Consensus B c03/28/20193 aWe 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.

1 aGupta, Nirupam1 aKatz, Jonathan1 aChopra, Nikhil uhttps://arxiv.org/abs/1809.0179406429nas a2200121 4500008004100000245006700041210006600108520603900174100001906213700001906232700001906251856003706270 2018 eng d00aInformation-Theoretic Privacy in Distributed Average Consensus0 aInformationTheoretic Privacy in Distributed Average Consensus3 aWe propose an asynchronous distributed average consensus algorithm that guarantees information-theoretic privacy of honest agents' inputs against colluding semi-honest (passively adversarial) agents, as long as the set of colluding semi-honest agents is not a vertex cut in the underlying communication network. This implies that a network with