User-generated trajectories (e.g. during traveling) can be leveraged to offer value-added services (e.g. smart city policy formulation), but there are also privacy implications. For example, information about the routes or destinations obtained from such published trajectories can be used to profile and identify users, including during contact tracing in pandemics (e.g., COVID-19) or the monitoring of demonstrations (e.g., surveillance). However, existing trajectory publishing algorithms generally rely on batch processing platforms, and rarely pay attention to real-time privacy protection processing in streaming scenarios. Therefore, we propose a stream processing framework containing two modules for spatio-temporal data. This framework is designed to achieve high data utility, while effectively ensuring the preservation of privacy in the published results. The first module is TSP, which concurrently receives real-time queries from individuals and releases new sanitizing trajectories. The second module is VCR comprising three algorithms based on differential privacy to facilitate the publication of the distribution of position statistics. Our experiments on real-world datasets demonstrate that our framework can effectively guarantee privacy with high data utility, when the appropriate parameter configuration is chosen. In addition, compared with the baseline algorithm $H^t$-publication, our group-based algorithm ${\mathit{AG}}^n$-publication achieves better data accuracy in terms of visitor counts at the same level of privacy protection.

用户生成的轨迹（例如，在旅途中）可用于提供增值服务（例如，智慧城市政策制定），但也存在隐私隐患。例如，关于从这样的已发布的轨迹获得的路线或目的地的信息，可用于概要分析和识别用户，包括在大流行病（例如COVID-19）的接触者追踪或示威监视（例如监视）期间。但是，现有的轨迹发布算法通常依赖于批处理平台，很少关注流场景中的实时隐私保护处理。因此，我们提出了一个流处理框架，其中包含两个用于时空数据的模块。该框架旨在实现高数据实用性，同时有效地确保发布结果中的隐私保护。第一个模块是TSP，它同时接收个人的实时查询并发布新的消毒轨迹。第二模块是VCR，包括基于差分隐私的三种算法，以促进位置统计信息的发布。我们对现实世界的数据集实验表明，我们的架构可以有效保证私密性较高的数据工具，在选择合适的参数配置。另外，与基线算法相比 第二模块是VCR，包括基于差分隐私的三种算法，以促进位置统计信息的发布。我们对现实世界的数据集实验表明，我们的架构可以有效保证私密性较高的数据工具，在选择合适的参数配置。另外，与基线算法相比 第二模块是VCR，包括基于差分隐私的三种算法，以促进位置统计信息的发布。我们对现实世界的数据集实验表明，我们的架构可以有效保证私密性较高的数据工具，在选择合适的参数配置。另外，与基线算法相比$H^{Ť}$-发布，我们基于组的算法${\mathit{股份公司}}^{ñ}$-出版实现了在保护隐私的同级别访问者人数方面更好的数据的准确性。