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.

翻译：集成感知与通信（ISAC）技术通过轨迹共享增强波束赋形、资源分配与协同感知能力，但依据《通用数据保护条例》（GDPR）的数据最小化原则，此举引发了根本性的隐私关切。本文提出一种基于费雪信息密度（FID）约束的轨迹共享框架，该框架对估计不确定性施加局部下界，从而从结构上提供硬性、可量化的隐私保障。与固定噪声方法不同，所提方法能够限制隐私泄露率（PLR），使其不受感知功率或对抗性后处理的影响，确保任何轨迹片段的重建精度均无法突破预设阈值。基于OpenTraj数据集的仿真表明，该框架在保持平均PLR低于20%-25%的同时，将最大泄漏片段持续时间控制在2-2.5秒以内，并保留运动预测等下游任务的数据效用。最终形成的准则具有可解释性、模型无关性，且兼容符合GDPR要求的ISAC系统设计。