Collaborative Compressive Detection with Physical Layer Secrecy Constraints

TL;DR

This paper introduces a collaborative compressive detection framework that balances high-dimensional signal detection performance with physical layer secrecy, using artificial noise and optimal parameter tuning to thwart eavesdroppers.

Contribution

It proposes a novel CCD framework with analytical trade-offs, integrating artificial noise injection for physical layer secrecy in compressed sensing-based detection.

Findings

Analytical characterization of the trade-off between compression and detection performance.

Demonstration that CS can enhance security by aiding secrecy.

Identification of optimal parameters for maximizing detection while ensuring secrecy.

Abstract

This paper considers the problem of detecting a high dimensional signal (not necessarily sparse) based on compressed measurements with physical layer secrecy guarantees. First, we propose a collaborative compressive detection (CCD) framework to compensate for the performance loss due to compression with a single sensor. We characterize the trade-off between dimensionality reduction achieved by a universal compressive sensing (CS) based measurement scheme and the achievable performance of CCD analytically. Next, we consider a scenario where the network operates in the presence of an eavesdropper who wants to discover the state of the nature being monitored by the system. To keep the data secret from the eavesdropper, we propose to use cooperating trustworthy nodes that assist the fusion center (FC) by injecting artificial noise to deceive the eavesdropper. We seek the answers to the questions: Does CS help improve the security performance in such a framework? What are the optimal values of parameters which maximize the CS based collaborative detection performance at the FC while ensuring perfect secrecy at the eavesdropper?