  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.
