Differentially Private Recursive Least Squares Estimation for ARX Systems with Multi-Participants

TL;DR

This paper introduces a differentially private recursive least squares algorithm for multi-participant ARX systems, ensuring privacy while maintaining estimation accuracy under various conditions.

Contribution

It provides a rigorous privacy analysis, establishes the relationship between noise and privacy level, and demonstrates the algorithm's effectiveness without requiring strict assumptions on data.

Findings

The algorithm guarantees differential privacy with asymptotic stability.

Estimation error bounds are derived under weak excitation conditions.

The existence of optimal noise levels minimizes estimation error.

Abstract

This paper proposes a differentially private recursive least squares algorithm to estimate the parameter of autoregressive systems with exogenous inputs and multi-participants (MP-ARX systems) and protect each participant's sensitive information from potential attackers. We first give a rigorous differential privacy analysis of the algorithm, and establish the quantitative relationship between the added noises and the privacy-preserving level when the system is asymptotically stable. The asymptotic stability of the system is necessary for ensuring the differential privacy of the algorithm. We then give an estimation error analysis of the algorithm under the general and possible weakest excitation condition without requiring the boundedness, independence and stationarity on the regression vectors. Particularly, when there is no regression term in the system output and the differential privacy only on the system output is considered, $\varepsilon$-differential privacy and almost sure convergence of the algorithm can be established simultaneously. To minimize the estimation error of the algorithm with $\varepsilon$-differential privacy, the existence of the noise intensity is proved. Finally, two examples are given to show the efficiency of the algorithm.