With low-cost computing devices, improved sensor technology, and the proliferation of data-driven algorithms, we have more data than we know what to do with. In transportation, we are seeing a surge in spatiotemporal data collection. At the same time, concerns over user privacy have led to research on differential privacy in applied settings. In this paper, we look at some recent developments in differential privacy in the context of spatiotemporal data. Spatiotemporal data contain not only features about users but also the geographical locations of their frequent visits. Hence, the public release of such data carries extreme risks. To address the need for such data in research and inference without exposing private information, significant work has been proposed. This survey paper aims to summarize these efforts and provide a review of differential privacy mechanisms and related software. We also discuss related work in transportation where such mechanisms have been applied. Furthermore, we address the challenges in the deployment and mass adoption of differential privacy in transportation spatiotemporal data for downstream analyses.

翻译：随着低成本计算设备的普及、传感器技术的改进以及数据驱动算法的广泛应用，我们拥有的数据已远超处理能力。在交通领域，时空数据采集正呈现爆发式增长。与此同时，对用户隐私的担忧推动了差分隐私在应用场景中的研究。本文聚焦于时空数据背景下差分隐私的最新进展。时空数据不仅包含用户特征信息，还记录其频繁访问的地理位置，因此此类数据的公开发布存在极高风险。为满足研究推断对数据的需求同时避免隐私泄露，学界已提出诸多重要方案。本综述旨在系统梳理相关成果，对差分隐私机制及其配套软件进行评述。我们还探讨了交通领域中此类机制的应用实例，并进一步分析了差分隐私在交通时空数据下游分析中大规模部署与普及所面临的挑战。