A PCA-Based Framework Leveraging frequency-dependent Piezoelectric Impedance for Macro-Scale PUFs

Abstract

Piezoelectric sensors are widely employed in structural health monitoring of safety-critical systems including aerospace, industrial IoT, and military applications, because of their ability to transduce mechanical activity into electrical signals. These sensors relay sensitive information about potential damages to critical infrastructure, thereby motivating the need to embed the root of trust into the sensors themselves. This work proposes a secure ID generation framework leveraging the frequency-dependent impedance response of commercial off-the-shelf (COTS) piezoelectric sensors to create Physical Unclonable Functions (PUF). Manufacturing imperfections introduce stable, device-specific perturbations in the impedance characteristics. When analyzed across a sensor population using Principal Component Analysis (PCA) yield discriminative 64-bit IDs. To support experimental validation, a 70-sensor dataset was assembled spanning multiple temperature conditions and a multi-month data collection period, capturing realistic temporal drift and environmental variability. Experimental results demonstrate near-ideal uniqueness (50.32%) and a significant improvement in reliability compared to existing methods. This framework successfully demonstrates how widely deployed piezoelectric sensors can serve as practical, low-overhead security primitives without excessive hardware modification.