The generation of trajectory data has increased dramatically with the advent and widespread use of GPS-enabled devices. This rich source of data provides invaluable insights for various applications such as traffic optimization, urban planning, crowd management, and public safety. However, the increasing demand for the publication and sharing of trajectory data for big data analytics raises significant privacy concerns due to the sensitive nature of the location information embedded in the trajectory data. Privacy-preserving trajectory publishing (PPTP) has been an active research area to address these concerns, and synthetic trajectory generation has emerged as a promising direction within PPTP. This survey paper provides a comprehensive overview of PPTP with a focus on synthetic trajectory generation methods, which have been insufficiently covered in previous surveys. Our contributions include a comparison of existing PPTP techniques based on their applicability and effectiveness for data analysis tasks. We then review and discuss the existing work on synthetic trajectory generation in the context of PPTP. Specifically, we classify the existing studies into two main categories, algorithm-based and deep learning-based approaches, and within each category, we perform a comparative analysis of the studied methods, focusing on their different characteristics. Finally, in order to encourage further research in this area, we identify and highlight a number of promising directions for future investigation that deserve to be explored in greater depth.

中文翻译：

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合成轨迹微数据的隐私保护生成和发布：一项综合调查


随着 GPS 设备的出现和广泛使用，轨迹数据的生成急剧增加。这种丰富的数据源为交通优化、城市规划、人群管理和公共安全等各种应用提供了宝贵的见解。然而，由于轨迹数据中嵌入的位置信息的敏感性，对大数据分析轨迹数据的发布和共享的需求不断增长，引发了严重的隐私问题。隐私保护轨迹发布（PPTP）一直是解决这些问题的一个活跃的研究领域，而合成轨迹生成已成为 PPTP 中一个有前途的方向。本调查论文对 PPTP 进行了全面概述，重点关注合成轨迹生成方法，而这些方法在之前的调查中并未得到充分涵盖。我们的贡献包括根据现有 PPTP 技术对数据分析任务的适用性和有效性进行比较。然后，我们回顾并讨论 PPTP 背景下合成轨迹生成的现有工作。具体来说，我们将现有的研究分为两大类：基于算法的方法和基于深度学习的方法，并且在每一类中，我们对所研究的方法进行比较分析，重点关注它们的不同特点。最后，为了鼓励该领域的进一步研究，我们确定并强调了未来研究的一些有希望的方向，值得更深入地探索。