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.

翻译：无处不在的泛在定位设备与移动互联网的普及，使得我们能够收集海量个体级轨迹数据集。这类轨迹大数据为人类移动性研究带来新机遇，但也引发了公众对位置隐私的担忧。本文提出条件对抗轨迹合成（CATS）框架——一种基于深度学习的GeoAI方法论框架，用于隐私保护的轨迹数据生成与发布。CATS将K-匿名性应用于人类移动的时空分布底层，提供了分布级的强隐私保障。通过对K-匿名化人类移动矩阵进行条件对抗训练、利用注意力机制学习轨迹全局上下文特征、以及采用相邻轨迹点的递归二分图匹配方法，CATS能够从条件采样位置重建轨迹拓扑结构，生成高质量的个体级合成轨迹数据，可作为原始数据的补充或替代品用于隐私保护轨迹数据发布。在超过9万个GPS轨迹上的实验结果表明，与基线方法相比，本方法在隐私保护、时空特征保持及下游任务效用方面表现更优，为利用生成式AI技术开展隐私保护人类移动性研究提供了新视角，并探索了地理信息科学中的数据伦理问题。