GDPR-Aware Trajectory Sharing for ISAC-Assisted Robot Navigation: A Case Study on FID-Constrained Collision Prediction
2026-07-03 • Robotics
Robotics
AI summaryⓘ
The authors study how to share robot position info safely to avoid collisions without revealing too much personal movement data. They propose a method that adds noise based on how much information is in the data before sharing it, which helps limit privacy risks. Testing on real walking paths shows their approach better balances keeping data useful and protecting privacy compared to just adding fixed noise. This method follows GDPR rules about limiting and safeguarding personal data.
Integrated Sensing and Communication (ISAC)General Data Protection Regulation (GDPR)Fisher Information Density (FID)trajectory sharingprivacy-utility tradeoffdata minimisationcollision avoidanceperturbationreconstruction leakagerobot sensing
Authors
Zexin Fang, Bin Han, Donglin Wang, Fengchen Pei, Hans D. Schotten
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.