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• W4306885655 abstract "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." @default.
• W4306885655 created "2022-10-21" @default.
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• W4306885655 date "2022-12-05" @default.
• W4306885655 modified "2023-10-07" @default.
• W4306885655 title "Reconstruction Attack on Differential Private Trajectory Protection Mechanisms" @default.
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• W4306885655 doi "https://doi.org/10.1145/3564625.3564628" @default.
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