We estimate vehicular traffic states from multimodal data collected by single-loop detectors while preserving the privacy of the individual vehicles contributing to the data. To this end, we propose a novel hybrid differential privacy (DP) approach that utilizes minimal randomization to preserve privacy by taking advantage of the relevant traffic state dynamics and the concept of DP sensitivity. Through theoretical analysis and experiments with real-world data, we show that the proposed approach significantly outperforms the related baseline non-private and private approaches in terms of accuracy and privacy preservation.

翻译：我们通过单回路检测器收集的多模态数据估计车辆交通状态，同时保护提供数据的个体车辆的隐私。为此，我们提出一种新颖的混合差分隐私方法，该方法利用相关交通状态动态和差分隐私敏感度概念，通过最小化随机化来保护隐私。通过理论分析和真实世界数据实验，我们证明所提出的方法在准确性和隐私保护方面显著优于相关的基线非隐私和隐私方法。