Location trajectories collected by smartphones and other devices represent a valuable data source for applications such as location-based services. Likewise, trajectories have the potential to reveal sensitive information about individuals, e.g., religious beliefs or sexual orientations. Accordingly, trajectory datasets require appropriate sanitization. Due to their strong theoretical privacy guarantees, differential private publication mechanisms receive much attention. However, the large amount of noise required to achieve differential privacy yields structural differences, e.g., ship trajectories passing over land. We propose a deep learning-based Reconstruction Attack on Protected Trajectories (RAoPT), that leverages the mentioned differences to partly reconstruct the original trajectory from a differential private release. The evaluation shows that our RAoPT model can reduce the Euclidean and Hausdorff distances between the released and original trajectories by over 68% on two real-world datasets under protection with $\varepsilon \leq 1$. In this setting, the attack increases the average Jaccard index of the trajectories' convex hulls, representing a user's activity space, by over 180%. Trained on the GeoLife dataset, the model still reduces the Euclidean and Hausdorff distances by over 60% for T-Drive trajectories protected with a state-of-the-art mechanism ($\varepsilon = 0.1$). This work highlights shortcomings of current trajectory publication mechanisms, and thus motivates further research on privacy-preserving publication schemes.
翻译:智能手机和其他装置收集的定位轨迹是用于定位服务等应用的宝贵数据源。 同样, 轨迹有可能披露个人敏感信息, 例如宗教信仰或性取向。 因此, 轨迹数据集需要适当的消毒。 由于其强大的理论隐私保障, 不同的私人发布机制受到极大关注。 然而, 实现差异隐私系统所需的大量噪音将产生结构性差异, 例如, 船舶轨迹通过陆地。 我们提议在受保护轨迹上进行基于深度学习的重建攻击( RAoPT), 利用上述轨迹差异从差异私人发布中部分重建原始轨迹。 评估显示, 我们的轨迹数据集模型可以将Euclidean和Hausdorf之间的距离减少68%以上, 在两个真实世界数据集中, 以 $varepsilon=lef$1美元来保护。 在此环境中, 攻击会进一步增加当前轨迹轨迹轨迹的轨迹上的轨迹定位索引( RAPPT), 以 80% 的轨迹模型和 IMLLI 的系统, 将减少用户的太空空间活动。