IPRU: Input-Perturbation-based Radio Frequency Fingerprinting Unlearning for LAWNs

TL;DR

The paper introduces IPRU, a lightweight input-perturbation method for unlearning specific AAV fingerprints in RF-based authentication, improving efficiency and privacy without retraining models.

Contribution

It proposes a novel unlearning scheme using a universal Fingerprint Forget Vector that erases target AAV fingerprints efficiently without altering model parameters.

Findings

Achieves 1.41% unlearning accuracy and 99.41% remaining accuracy.

Runs 5.79 times faster than retraining methods.

Provides 100% resistance to membership inference attacks.

Abstract

Radio Frequency Fingerprinting (RFF) is a key technology for identity authentication in wireless networks. However, due to the rapid dynamics of Autonomous Aerial Vehicles (AAVs) in low-altitude wireless networks, RFF models require parameter updates to maintain authentication performance, posing a major challenge to existing schemes. Conventional retraining approaches for handling departed or compromised AAVs are computationally prohibitive and risk retaining polluted features, which compromises both authentication security and user privacy. To address these limitations, we propose an Input-Perturbation-based RFF Unlearning (IPRU) scheme. By optimizing a universal Fingerprint Forget Vector (FFV) as a lightweight input perturbation, IPRU successfully erases the fingerprints of target AAVs without modifying the RFF model parameters, achieving an effective balance between efficient unlearning and preserved authentication performance. A combinatorial optimization strategy further enables multi-AAV forgetting on demand. The simulation results demonstrate that IPRU achieves 1.41% unlearning accuracy, 99.41% remaining accuracy, and 100% resistance to membership inference attack, while running 5.79X faster than retraining and 2.1X faster than the baseline scheme.