GDPR-Aware Trajectory Sharing for ISAC-Assisted Robot Navigation: A Case Study on FID-Constrained Collision Prediction

2026-07-03

AI Summary

This study addresses the challenge of sharing high-precision trajectories in ISAC-assisted robotic navigation while complying with GDPR requirements, particularly balancing data minimization against unauthorized trajectory reconstruction. The authors propose an adaptive trajectory perturbation mechanism constrained by Fisher Information Density (FID), introducing FID as a novel control metric to dynamically adjust perturbation intensity based on local information content. This approach enhances privacy protection without compromising obstacle avoidance performance. Evaluated on real pedestrian trajectory datasets and validated through a collision prediction model, the method significantly reduces both trajectory reconstructability and sustained exposure duration compared to fixed-error perturbation, achieving superior privacy-utility trade-offs at equivalent false negative rates. The design strictly adheres to GDPR principles of data minimization and integrity.

Abstract

Integrated sensing and communication (ISAC) enables intelligent wireless infrastructure but raises growing regulatory concern as fine-grained personal trajectory histories become a byproduct of sensing. General Data Protection Regulation (GDPR) Articles 5(1)(c) and 5(1)(f) require that personal data be limited to what is necessary and protected through appropriate technical measures against unauthorised reconstruction. This paper addresses both requirements through a Fisher information density (FID)-constrained trajectory sharing scheme for robot collision avoidance, where sensing estimates are perturbed according to local information content before sharing. Experiments on real pedestrian traces show that FID-controlled sharing achieves a strictly better privacy-utility tradeoff than fixed-error perturbation: at matched missed-conflict rates, reconstruction leakage and sustained exposure lengths are consistently lower, establishing information-aware perturbation as a principled technical measure aligned with GDPR data minimisation and integrity requirements.

Problem

• GDPR
• trajectory sharing
• privacy
• ISAC
• data minimisation

Innovation

• Fisher Information Density
• GDPR-compliant trajectory sharing
• ISAC
• privacy-utility tradeoff
• information-aware perturbation