  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.
