Trajectory streams are being generated from location-aware devices, such as smartphones and in-vehicle navigation systems. Due to the sensitive nature of the location data, directly sharing user trajectories suffers from privacy leakage issues. Local differential privacy (LDP), which perturbs sensitive data on the user side before it is shared or analyzed, emerges as a promising solution for private trajectory stream collection and analysis. Unfortunately, existing stream release approaches often neglect the rich spatial-temporal context information within trajectory streams, resulting in suboptimal utility and limited types of downstream applications. To this end, we propose RetraSyn, a novel real-time trajectory synthesis framework, which is able to perform on-the-fly trajectory synthesis based on the mobility patterns privately extracted from users' trajectory streams. Thus, the downstream trajectory analysis can be performed on the high-utility synthesized data with privacy protection. We also take the genuine behaviors of real-world mobile travelers into consideration, ensuring authenticity and practicality. The key components of RetraSyn include the global mobility model, dynamic mobility update mechanism, real-time synthesis, and adaptive allocation strategy. We conduct extensive experiments on multiple real-world and synthetic trajectory datasets under various location-based utility metrics, encompassing both streaming and historical scenarios. The empirical results demonstrate the superiority and versatility of our proposed framework.

中文翻译：

---

具有本地差分隐私的实时轨迹合成

轨迹流是由智能手机和车载导航系统等位置感知设备生成的。由于位置数据的敏感性，直接共享用户轨迹会遇到隐私泄露问题。本地差分隐私（LDP）在共享或分析之前扰乱用户端的敏感数据，成为隐私轨迹流收集和分析的一种有前景的解决方案。不幸的是，现有的流发布方法经常忽略轨迹流中丰富的时空上下文信息，导致实用性次优和下游应用程序类型有限。为此，我们提出了 RetraSyn，一种新颖的实时轨迹合成框架，它能够基于从用户轨迹流中私下提取的移动模式来执行动态轨迹合成。因此，可以在隐私保护的情况下对高利用率的合成数据进行下游轨迹分析。我们还考虑了现实世界移动旅行者的真实行为，确保真实性和实用性。 RetraSyn的关键组件包括全局移动性模型、动态移动性更新机制、实时综合和自适应分配策略。我们在各种基于位置的效用指标下对多个现实世界和合成轨迹数据集进行了广泛的实验，涵盖流媒体和历史场景。实证结果证明了我们提出的框架的优越性和多功能性。