Balancing Functionality and GDPR-Driven Privacy in ISAC Trajectory Sharing

TL;DR

This paper introduces a privacy-preserving trajectory sharing framework for ISAC systems that guarantees GDPR compliance by bounding estimation uncertainty and privacy leak ratio, ensuring data utility and privacy.

Contribution

It proposes a Fisher Information Density-constrained approach that provides hard privacy guarantees, unlike fixed-noise methods, applicable across sensing scenarios.

Findings

Keeps average PLR below 20-25%

Limits maximum leakage segment duration to 2-2.5 seconds

Maintains data utility for movement prediction tasks

Abstract

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.