Privacy in networks of quantum sensors

TL;DR

This paper investigates privacy in quantum sensor networks, analyzing how to maximize privacy in parameter estimation using quantum Fisher information, and introduces the concept of quasi-privacy under noise conditions.

Contribution

It develops a framework for analyzing and optimizing privacy in quantum sensor networks using quantum Fisher information, including the impact of noise and the novel quasi-privacy concept.

Findings

Optimal states for maximum privacy identified

Noise reduces privacy effectiveness

Quasi-privacy quantifies near-privacy states

Abstract

We treat privacy in a network of quantum sensors where accessible information is limited to specific functions of the network parameters, and all other information remains private. We develop an analysis of privacy in terms of a manipulation of the quantum Fisher information matrix, and find the optimal state achieving maximum privacy in the estimation of linear combination of the unknown parameters in a network of quantum sensors. We also discuss the effect of uncorrelated noise on the privacy of the network. Moreover, we illustrate our results with an example where the goal is to estimate the average value of the unknown parameters in the network. In this example, we also introduce the notion of quasi-privacy ($\epsilon$-privacy), quantifying how close the state is to being private.