LSTM-TrajGAN: A Deep Learning Approach to Trajectory Privacy Protection
This work addresses privacy concerns for users of location-based services by providing a method to protect trajectory data during sharing and publication, though it is incremental as it builds on existing GAN and LSTM approaches.
The authors tackled the problem of individual trajectory data privacy by proposing LSTM-TrajGAN, a deep learning model that generates synthetic trajectory data to prevent user re-identification while preserving spatial, temporal, and thematic characteristics, outperforming common geomasking methods in evaluations.
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.