Balancing Functionality and GDPR-Driven Privacy in ISAC Trajectory Sharing
For ISAC system designers, this work addresses the GDPR data minimisation principle with a provable privacy guarantee, though the problem is specific to trajectory sharing in ISAC.
The paper proposes a Fisher Information Density (FID)-constrained trajectory sharing framework for ISAC that provides hard, quantifiable privacy guarantees under GDPR. Simulations on OpenTraj show average Privacy Leak Ratio below 20-25% and maximum leakage segment duration under 2-2.5 s while preserving utility.
Integrated Sensing and Communications (ISAC) enables trajectory sharing that enhances beamforming, resource allocation, and cooperative perception, yet raises fundamental privacy concerns under the General Data Protection Regulation (GDPR) data minimisation principle. This paper proposes a Fisher Information Density (FID)-constrained trajectory sharing framework that enforces a local lower bound on estimation uncertainty, providing hard, quantifiable privacy guarantees by construction. Unlike fixed-noise approaches, the proposed method bounds the Privacy Leak Ratio (PLR) regardless of sensing power or adversarial post-processing, ensuring that no trajectory segment can be reconstructed beyond a prescribed accuracy threshold. Simulations on the OpenTraj dataset demonstrate that the framework keeps the average PLR below 20-25% and the maximum leakage segment duration under 2-2.5 s, while preserving data utility for downstream tasks such as movement prediction. The resulting criterion is interpretable, model-agnostic, and compatible with GDPR-compliant ISAC system design.