Maximizing Privacy in MIMO Cyber-Physical Systems Using the Chapman-Robbins Bound

TL;DR

This paper investigates how to maximize privacy in MIMO cyber-physical systems by using additive Gaussian noise, deriving fundamental bounds, and proposing methods to enhance privacy while balancing utility, validated through real-world data.

Contribution

The paper introduces a novel approach to quantify and improve privacy in linear systems using the Chapman-Robbins bound, linking privacy to system zeros and optimizing input privacy.

Findings

Derived lower bounds on privacy using Chapman-Robbins bound

Connected privacy levels to transmission zeros of the system

Validated privacy-utility trade-off with real data from KTH Live-In Lab

Abstract

Privacy breaches of cyber-physical systems could expose vulnerabilities to an adversary. Here, privacy leaks of step inputs to linear-time-invariant systems are mitigated through additive Gaussian noise. Fundamental lower bounds on the privacy are derived, which are based on the variance of any estimator that seeks to recreate the input. Fully private inputs are investigated and related to transmission zeros. Thereafter, a method to increase the privacy of optimal step inputs is presented and a privacy-utility trade-off bound is derived. Finally, these results are verified on data from the KTH Live-In Lab Testbed, showing good correspondence with theoretical results.