用信息密度控制轨迹共享，兼顾隐私与导航精度。
GDPR-Aware Trajectory Sharing for ISAC-Assisted Robot Navigation: A Case Study on FID-Constrained Collision Prediction
- 根据局部信息密度动态扰动轨迹数据，实现精准隐私保护。
- 实测显示在相同漏报率下，隐私泄露和暴露时间更低。
- 适合关注合规的智能交通与机器人导航系统开发者。
集成感知与通信（ISAC）技术虽提升智能无线基础设施能力，但其感知过程会产生精细的个人轨迹数据，引发《通用数据保护条例》（GDPR）合规担忧。根据GDPR第5(1)(c)和5(1)(f)条，个人数据应最小化且通过适当技术措施防止未经授权重建。本文提出一种基于费舍尔信息密度（FID）约束的轨迹共享方案，对感知估计值按本地信息含量进行扰动后再共享，以满足数据最小化与完整性要求。基于真实行人轨迹的实验表明，相较于固定误差扰动，FID控制的共享方式在相同漏检冲突率下，能持续降低重构泄漏与暴露时长，证明信息感知型扰动是符合GDPR要求的可操作技术手段。
原文摘要 · Abstract (English)
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.
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