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LSTM-TrajGAN: A Deep Learning Approach to Trajectory Privacy Protection

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arxiv 2006.10521 v1 pith:LQKMVDEJ submitted 2020-06-14 cs.LG cs.AIcs.CR

Jinmeng Rao , Song Gao , Yuhao Kang , Qunying Huang This is my paper · ORCID
classification cs.LGcs.AIcs.CR
keywords trajectorymodeldataprivacyprotectionapproachbetterdeep
verification ladder T0 review T1 audit T2 compute T3 formal
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The prevalence of location-based services contributes to the explosive growth of individual-level trajectory data and raises public concerns about privacy issues. In this research, we propose a novel LSTM-TrajGAN approach, which is an end-to-end deep learning model to generate privacy-preserving synthetic trajectory data for data sharing and publication. We design a loss metric function TrajLoss to measure the trajectory similarity losses for model training and optimization. The model is evaluated on the trajectory-user-linking task on a real-world semantic trajectory dataset. Compared with other common geomasking methods, our model can better prevent users from being re-identified, and it also preserves essential spatial, temporal, and thematic characteristics of the real trajectory data. The model better balances the effectiveness of trajectory privacy protection and the utility for spatial and temporal analyses, which offers new insights into the GeoAI-powered privacy protection.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Leveraging the Spatial Hierarchy: Coarse-to-fine Trajectory Generation via Cascaded Hybrid Diffusion

    cs.SI 2025-07 conditional novelty 6.0 of 10

    A cascaded hybrid diffusion framework generates coarse road-segment trajectories first, then high-fidelity GPS trajectories conditioned on them, outperforming trajectory-synthesis baselines on JSD metrics.

  2. Towards Physics-informed Diffusion for Anomaly Detection in Trajectories

    cs.LG 2025-06 conditional novelty 5.0 of 10

    A diffusion model regularized with kinematic bicycle constraints detects synthetic trajectory anomalies more accurately than prior methods, but the evaluation depends on anomalies that match the physics prior.

  3. Ctx2TrajGen: Traffic Context-Aware Microscale Vehicle Trajectories using Generative Adversarial Imitation Learning

    cs.AI 2025-07 reject novelty 4.0 of 10

    Ctx2TrajGen combines GAIL, PPO, WGAN-GP, GRU, and GMM to generate context-aware microscale vehicle trajectories, reporting MMD 0.0021 and KL 1.2543 on the DRIFT site C subset.

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