  The prevalence of ubiquitous location-aware devices and mobile Internet
enables us to collect massive individual-level trajectory dataset from users.
Such trajectory big data bring new opportunities to human mobility research but
also raise public concerns with regard to location privacy. In this work, we
present the Conditional Adversarial Trajectory Synthesis (CATS), a
deep-learning-based GeoAI methodological framework for privacy-preserving
trajectory data generation and publication. CATS applies K-anonymity to the
underlying spatiotemporal distributions of human movements, which provides a
distributional-level strong privacy guarantee. By leveraging conditional
adversarial training on K-anonymized human mobility matrices, trajectory global
context learning using the attention-based mechanism, and recurrent bipartite
graph matching of adjacent trajectory points, CATS is able to reconstruct
trajectory topology from conditionally sampled locations and generate
high-quality individual-level synthetic trajectory data, which can serve as
supplements or alternatives to raw data for privacy-preserving trajectory data
publication. The experiment results on over 90k GPS trajectories show that our
method has a better performance in privacy preservation, spatiotemporal
characteristic preservation, and downstream utility compared with baseline
methods, which brings new insights into privacy-preserving human mobility
research using generative AI techniques and explores data ethics issues in
GIScience.
