



01408nas a2200133   4500008004100000245008000041210006900121260001500190520097500205100001901180700001901199700001901218856003701237       2019                        eng d00aStatistical Privacy in Distributed Average Consensus on Bounded Real Inputs0 aStatistical Privacy in Distributed Average Consensus on Bounded   c03/20/20193 a<p>This paper proposes a privacy protocol for distributed average consensus algorithms on bounded real-valued inputs that guarantees statistical privacy of honest agents&#39; 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&#39; 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&#39; inputs, where bounds are known apriori to all the agents.&nbsp;</p>1 aGupta, Nirupam1 aKatz, Jonathan1 aChopra, Nikhil  uhttps://arxiv.org/abs/1903.09315