Encrypted distributed state estimation via affine averaging

TL;DR

This paper introduces an encrypted affine averaging algorithm for distributed state estimation that preserves privacy using homomorphic encryption, addressing overflow issues with state resets and validating the approach through simulations.

Contribution

It presents a novel encrypted implementation of affine averaging for distributed estimation, ensuring privacy and handling overflow with reset strategies.

Findings

Encrypted affine averaging maintains estimation accuracy.

Reset strategies effectively prevent overflow issues.

Numerical simulations validate the proposed method.

Abstract

Distributed state estimation arises in many applications such as position estimation in robot swarms, clock synchronization for processor networks, and data fusion. One characteristic is that agents only have access to noisy measurements of deviations between their own and neighboring states. Still, estimations of their actual state can be obtained in a fully distributed manner using algorithms such as affine averaging. However, running this algorithm, requires that the agents exchange their current state estimations, which can be a privacy issue (since they eventually reveal the actual states). To counteract this threat, we propose an encrypted version of the affine averaging algorithm in this paper. More precisely, we use homomorphic encryption to realize an encrypted implementation, where only one ``leader'' agent has access to its state estimation in plaintext. One main challenge (which often arises for recursive encrypted computations) is to prevent overflow w.r.t.~the bounded message space of the cryptosystem. We solve this problem by periodically resetting the agents' states with the help of the leader. We study the resulting system dynamics with respect to different reset strategies and support our findings with extensive numerical simulations.