用于车辆交通分析的多模态数据的高效隐私保护处理
Efficient Privacy-Preserved Processing of Multimodal Data for Vehicular Traffic Analysis
摘要：
我们估计车辆的交通状态，从单回路检测器收集的多模态数据，同时保护个人车辆的隐私，有助于数据。为此，我们提出了一种新的混合差分隐私（DP）的方法，利用最小的随机化，以保护隐私，利用相关的交通状态动态和DP敏感性的概念。通过理论分析和实验与真实世界的数据，我们表明，所提出的方法显着优于相关的基线非私人和私人的方法在准确性和隐私保护。
英文摘要：
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.
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