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.

利用无处不在的位置感知设备和移动互联网收集用户的海量个体轨迹数据，基于深度学习的GeoAI方法论框架——条件对抗轨迹合成（CATS）提供了保护隐私的轨迹数据生成和发布解决方案，该方法应用K-匿名来保护人类移动行为的时空分布，通过条件对抗训练、注意力机制和轨迹拓扑的重建，生成高质量的合成轨迹数据，同时提供更好的隐私保护性能和空间-时间特征保持，为基于生成性人工智能技术的隐私保护人类移动研究带来新的视角，并探讨地理信息科学中的数据伦理问题。

CATS: 条件对抗轨迹合成 用于隐私保护轨迹数据发布的深度学习方法

CATS: 条件对抗轨迹合成用于隐私保护轨迹数据发布的深度学习方法