Privacy-Preserving Distributed Average Consensus in Finite Time using Random Gossip

TL;DR

This paper introduces a privacy-preserving distributed average consensus algorithm using random gossip, achieving finite-time convergence while protecting individual initial values from curious nodes in a fully-connected network.

Contribution

It proposes a novel privacy-preserving enhancement to gossip algorithms that guarantees privacy and finite-time convergence in distributed average consensus.

Findings

Achieves approximate average consensus in finite time.

Provides conditions for privacy preservation against colluding curious nodes.

Enables distributed termination detection of the consensus process.

Abstract

In this paper, we develop and analyze a gossip-based average consensus algorithm that enables all of the components of a distributed system, each with some initial value, to reach (approximate) average consensus on their initial values after executing a finite number of iterations, and without having to reveal the specific value they contribute to the average calculation. We consider a fully-connected (undirected) network in which each pair of components (nodes) can be randomly selected to perform pairwise standard gossip averaging of their values, and propose an enhancement that can be followed by each node that does not want to reveal its initial value to other (curious) nodes. We assume that curious nodes try to identify the initial values of other nodes but do not interfere in the computation in any other way; however, as a worst-case assumption, curious nodes are allowed to collaborate arbitrarily and are assumed to know the privacy-preserving strategy (but not the actual parameters chosen by the nodes that want to preserve their privacy). We characterize precisely conditions on the information exchange that guarantee privacy-preservation for a specific node. The protocol also provides a criterion that allows the nodes to determine, in a distributed manner (while running the enhanced gossip protocol), when to terminate their operation because approximate average consensus has been reached, i.e., all nodes have obtained values that are within a small distance from the exact average of their initial values.