Deterministic Privacy Preservation in Static Average Consensus Problem

TL;DR

This paper introduces an alternative iterative algorithm for static average consensus that inherently preserves privacy without additional overheads, matching the performance of traditional Laplacian-based methods.

Contribution

It demonstrates that a known dynamic average consensus algorithm can be adapted for static privacy preservation, eliminating the need for complex augmentations.

Findings

The proposed algorithm maintains the same convergence properties as Laplacian consensus.

It inherently preserves privacy without extra computational overhead.

The method simplifies privacy preservation in static average consensus.

Abstract

In this paper we consider the problem of privacy preservation in the static average consensus problem. This problem normally is solved by proposing privacy preservation augmentations for the popular first order Laplacian-based algorithm. These mechanisms however come with computational overhead, may need coordination among the agents to choose their parameters and also alter the transient response of the algorithm. In this paper we show that an alternative iterative algorithm that is proposed in the literature in the context of dynamic average consensus problem has intrinsic privacy preservation and can be used as a privacy preserving algorithm that yields the same performance behavior as the well-known Laplacian consensus algorithm but without the overheads that come with the existing privacy preservation methods.