Balancing Functionality and GDPR-Driven Privacy in ISAC Trajectory Sharing

2026-04-09

AI Summary

This work addresses the privacy risks in integrated sensing and communication (ISAC) systems, where trajectory sharing—while enhancing beamforming and collaborative perception—violates the GDPR principle of data minimization. To mitigate this, the authors propose a Fisher information density (FID)-constrained trajectory sharing framework that enforces a local lower bound on estimation uncertainty, ensuring that the reconstruction accuracy of any trajectory segment remains below a prescribed threshold. This model-agnostic approach, combined with trajectory perturbation, provides quantifiable and robust privacy guarantees: its privacy leakage ratio (PLR) remains unaffected by sensing power or adversarial post-processing. Experiments on the OpenTraj dataset demonstrate that the method keeps average PLR below 20–25%, limits maximum leakage duration to under 2.5 seconds, and preserves performance in downstream tasks such as motion prediction.

Technology Category

• Machine Learning: Privacy
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Security and Privacy: Data transparency and provenance
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Responsible Web: Data and user privacy-enhancing technologies for the Web

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.

Problem

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

Innovation

• Fisher Information Density
• Privacy Leak Ratio
• GDPR-compliant trajectory sharing
• Integrated Sensing and Communications
• data minimisation