Preserving the individuals' privacy in sharing spatial-temporal datasets is critical to prevent re-identification attacks based on unique trajectories. Existing privacy techniques tend to propose ideal privacy-utility tradeoffs, however, largely ignore the fairness implications of mobility models and whether such techniques perform equally for different groups of users. The quantification between fairness and privacy-aware models is still unclear and there barely exists any defined sets of metrics for measuring fairness in the spatial-temporal context. In this work, we define a set of fairness metrics designed explicitly for human mobility, based on structural similarity and entropy of the trajectories. Under these definitions, we examine the fairness of two state-of-the-art privacy-preserving models that rely on GAN and representation learning to reduce the re-identification rate of users for data sharing. Our results show that while both models guarantee group fairness in terms of demographic parity, they violate individual fairness criteria, indicating that users with highly similar trajectories receive disparate privacy gain. We conclude that the tension between the re-identification task and individual fairness needs to be considered for future spatial-temporal data analysis and modelling to achieve a privacy-preserving fairness-aware setting.

翻译：共享时空数据集时保护个体隐私对于防止基于独特轨迹的重识别攻击至关重要。现有隐私保护技术倾向于提出理想的隐私-效用权衡，但很大程度上忽视了移动模型的公平性影响，以及此类技术是否对不同用户群体表现均衡。公平性与隐私感知模型之间的量化关系仍不明确，且目前在时空背景下衡量公平性的指标集合几乎不存在。本文基于轨迹的结构相似性和熵，明确定义了一套专门针对人类移动性的公平性指标。在这些定义下，我们考察了两种最先进的隐私保护模型的公平性，这两种模型分别依赖生成对抗网络（GAN）和表示学习来降低数据共享中用户的重识别率。结果表明，尽管两种模型在人口统计均等性方面保证了群体公平性，但它们违反了个人公平性准则，表明轨迹高度相似的用户获得了不同的隐私增益。我们得出结论：未来时空数据分析与建模需考虑重识别任务与个人公平性之间的张力，以实现隐私保护下的公平感知环境。