As people's daily life becomes increasingly inseparable from various mobile electronic devices, relevant service application platforms and network operators can collect numerous individual information easily. When releasing these data for scientific research or commercial purposes, users' privacy will be in danger, especially in the publication of spatiotemporal trajectory datasets. Therefore, to avoid the leakage of users' privacy, it is necessary to anonymize the data before they are released. However, more than simply removing the unique identifiers of individuals is needed to protect the trajectory privacy, because some attackers may infer the identity of users by the connection with other databases. Much work has been devoted to merging multiple trajectories to avoid re-identification, but these solutions always require sacrificing data quality to achieve the anonymity requirement. In order to provide sufficient privacy protection for users' trajectory datasets, this paper develops a study on trajectory privacy against re-identification attacks, proposing a trajectory K-anonymity model based on Point Density and Partition (KPDP). Our approach improves the existing trajectory generalization anonymization techniques regarding trajectory set partition preprocessing and trajectory clustering algorithms. It successfully resists re-identification attacks and reduces the data utility loss of the k-anonymized dataset. A series of experiments on a real-world dataset show that the proposed model has significant advantages in terms of higher data utility and shorter algorithm execution time than other existing techniques.

翻译：随着人们的日常生活日益离不开各类移动电子设备，相关服务应用平台及网络运营商能够轻松收集大量个人信息。当出于科研或商业目的发布这些数据时，用户隐私将面临风险，尤其是在时空轨迹数据集发布场景中。因此，为避免用户隐私泄露，必须在数据发布前进行匿名化处理。然而，仅删除个人唯一标识符并不足以保护轨迹隐私，因为攻击者可能通过与其他数据库的关联推断用户身份。现有研究致力于合并多条轨迹以避免重识别攻击，但此类方案通常需要牺牲数据质量来满足匿名性要求。为给用户轨迹数据集提供充分的隐私保护，本文针对反重识别攻击的轨迹隐私问题展开研究，提出一种基于点密度与划分的轨迹K-匿名模型（KPDP）。该方法在轨迹集划分预处理和轨迹聚类算法方面改进了现有轨迹泛化匿名技术，成功抵御重识别攻击并降低K匿名数据集的数据效用损失。在真实数据集上的一系列实验表明，与现有技术相比，所提模型在更高的数据效用和更短的算法执行时间方面具有显著优势。