@article {2367, title = {Statistical Privacy in Distributed Average Consensus on Bounded Real Inputs}, year = {2019}, month = {03/20/2019}, abstract = {

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.\ 

}, url = {https://arxiv.org/abs/1903.09315}, author = {Nirupam Gupta and Jonathan Katz and Nikhil Chopra} } @article {2368, title = {Information-Theoretic Privacy For Distributed Average Consensus: Bounded Integral Inputs}, year = {2018}, month = {03/28/2019}, abstract = {

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.

}, url = {https://arxiv.org/abs/1809.01794}, author = {Nirupam Gupta and Jonathan Katz and Nikhil Chopra} } @article {2212, title = {Information-Theoretic Privacy in Distributed Average Consensus}, year = {2018}, abstract = {

We propose an asynchronous distributed average consensus algorithm that guarantees information-theoretic privacy of honest agents\&$\#$39; inputs against colluding semi-honest (passively adversarial) agents, as long as the set of colluding semi-honest agents is not a vertex cut in the underlying communication network. This implies that a network with\ (t+1)-connectivity guarantees information-theoretic privacy of honest agents\&$\#$39; inputs against any\ t\ colluding semi-honest agents. The proposed protocol is formed by composing a distributed privacy mechanism we provide with any (non-private) distributed average consensus algorithm.\ 

}, url = {https://arxiv.org/abs/1809.01794}, author = {Nirupam Gupta and Jonathan Katz and Nikhil Chopra} }