Differentially Private Iterative Synchronous Consensus

TL;DR

This paper introduces a differentially private iterative consensus protocol that enables agents to converge to a common value while protecting their initial states from adversaries, balancing privacy and accuracy.

Contribution

It adapts differential privacy to iterative consensus and proposes both server-based and distributed mechanisms with privacy-accuracy tradeoffs.

Findings

Protocols ensure convergence while maintaining privacy.

Mechanisms protect initial states from honest but curious adversaries.

Tradeoff analysis quantifies privacy versus accuracy.

Abstract

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. Protocols solving iterative consensus serve as building blocks in a variety of systems where distributed coordination is required for load balancing, data aggregation, sensor fusion, filtering, clock synchronization and platooning of autonomous vehicles. In this paper, we introduce the private iterative consensus problem where agents are required to converge while protecting the privacy of their initial values from honest but curious adversaries. Protecting the initial states, in many applications, suffice to protect all subsequent states of the individual participants. First, we adapt the notion of differential privacy in this setting of iterative computation. Next, we present a server-based and a completely distributed randomized mechanism for solving private iterative consensus with adversaries who can observe the messages as well as the internal states of the server and a subset of the clients. Finally, we establish the tradeoff between privacy and the accuracy of the proposed randomized mechanism.