Biblio

The iterative consensus problem requires a set of processes or agents with different initial values, to interact and update their states to eventually converge to a common value. Pro- tocols solving iterative consensus serve as building blocks in a variety of systems where distributed coordination is re- quired for load balancing, data aggregation, sensor fusion, filtering, and synchronization. In this paper, we introduce the private iterative consensus problem where agents are re- quired to converge while protecting the privacy of their ini- tial values from honest but curious adversaries. Protecting the initial states, in many applications, suffice to protect all subsequent states of the individual participants.

We adapt the notion of differential privacy in this setting of iterative computation. Next, we present (i) a server-based and (ii) a completely distributed randomized mechanism for solving differentially private iterative consensus with adver- saries who can observe the messages as well as the internal states of the server and a subset of the clients. Our analysis establishes the tradeoff between privacy and the accuracy.