



@article {2367,
	title = {Statistical Privacy in Distributed Average Consensus on Bounded Real Inputs},
	year = {2019},
	month = {03/20/2019},
	abstract = {<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>},
	url = {https://arxiv.org/abs/1903.09315},
	author = {Nirupam Gupta and Jonathan Katz and Nikhil Chopra}
}
