Dynamic Event-Triggered Discrete-Time Linear Time-Varying System with Privacy-Preservation

TL;DR

This paper develops a privacy-preserving, dynamic event-triggered set-membership estimator for discrete-time linear time-varying systems in wireless sensor networks, enhancing security and resource efficiency.

Contribution

It introduces a novel recursive convex optimization approach for differential privacy in set-membership estimation within time-varying systems.

Findings

The estimator effectively preserves privacy during state estimation.

The system's steady-state performance is theoretically analyzed.

An example demonstrates the estimator's feasibility and effectiveness.

Abstract

This paper focuses on discrete-time wireless sensor networks with privacy-preservation. In practical applications, information exchange between sensors is subject to attacks. For the information leakage caused by the attack during the information transmission process, privacy-preservation is introduced for system states. To make communication resources more effectively utilized, a dynamic event-triggered set-membership estimator is designed. Moreover, the privacy of the system is analyzed to ensure the security of the real data. As a result, the set-membership estimator with differential privacy is analyzed using recursive convex optimization. Then the steady-state performance of the system is studied. Finally, one example is presented to demonstrate the feasibility of the proposed distributed filter containing privacy-preserving analysis.