BACKGROUND Personal privacy is a significant concern in the era of big data. In the field of health geography, personal health data are collected with geographic location information which may increase disclosure risk and threaten personal geoprivacy. Geomasking is used to protect individuals' geoprivacy by masking the geographic location information, and spatial k-anonymity is widely used to measure the disclosure risk after geomasking is applied. With the emergence of individual GPS trajectory datasets that contains large volumes of confidential geospatial information, disclosure risk can no longer be comprehensively assessed by the spatial k-anonymity method. METHODS This study proposes and develops daily activity locations (DAL) k-anonymity as a new method for evaluating the disclosure risk of GPS data. Instead of calculating disclosure risk based on only one geographic location (e.g., home) of an individual, the new DAL k-anonymity is a composite evaluation of disclosure risk based on all activity locations of an individual and the time he/she spends at each location abstracted from GPS datasets. With a simulated individual GPS dataset, we present case studies of applying DAL k-anonymity in various scenarios to investigate its performance. The results of applying DAL k-anonymity are also compared with those obtained with spatial k-anonymity under these scenarios. RESULTS The results of this study indicate that DAL k-anonymity provides a better estimation of the disclosure risk than does spatial k-anonymity. In various case-study scenarios of individual GPS data, DAL k-anonymity provides a more effective method for evaluating the disclosure risk by considering the probability of re-identifying an individual's home and all the other daily activity locations. CONCLUSIONS This new method provides a quantitative means for understanding the disclosure risk of sharing or publishing GPS data. It also helps shed new light on the development of new geomasking methods for GPS datasets. Ultimately, the findings of this study will help to protect individual geoprivacy while benefiting the research community by promoting and facilitating geospatial data sharing.

中文翻译：

---

日常活动位置k匿名性用于评估单个GPS数据集的披露风险。

背景技术个人隐私是大数据时代中的重要关注。在健康地理领域，个人健康数据与地理位置信息一起收集，这可能会增加披露风险并威胁到个人地理隐私。地理掩蔽用于通过掩盖地理位置信息来保护个人的地理隐私，而空间k匿名性被广泛地用于应用地理掩蔽后的披露风险。随着包含大量机密地理空间信息的单个GPS轨迹数据集的出现，不再可以通过空间k匿名方法全面评估披露风险。方法本研究提出并发展了日常活动位置（DAL）k-匿名性，作为评估GPS数据公开风险的一种新方法。新的DAL k匿名不是基于个人的一个地理位置（例如，家）来计算披露风险，而是基于个人的所有活动位置以及他/她在每个人花费的时间对披露风险进行综合评估。从GPS数据集中提取的位置。借助模拟的单个GPS数据集，我们介绍了在各种情况下应用DAL k-匿名性以研究其性能的案例研究。在这些情况下，还将使用DAL k匿名性的结果与通过空间k匿名性获得的结果进行了比较。结果这项研究的结果表明，DAL k-匿名性比空间k-匿名性可以更好地估计披露风险。在各个GPS数据的各种案例研究场景中，DAL k匿名性通过考虑重新识别一个人的房屋和所有其他日常活动位置的可能性，提供了一种更有效的评估披露风险的方法。结论该新方法为理解共享或发布GPS数据的公开风险提供了一种定量方法。它还有助于为GPS数据集的新地理屏蔽方法的开发提供新的思路。最终，这项研究的结果将有助于保护个人的地理隐私，同时通过促进和促进地理空间数据共享来使研究社区受益。它还有助于为GPS数据集的新地理屏蔽方法的开发提供新的思路。最终，这项研究的结果将有助于保护个人的地理隐私，同时通过促进和促进地理空间数据共享来使研究社区受益。它还有助于为GPS数据集的新地理屏蔽方法的开发提供新的思路。最终，这项研究的结果将有助于保护个人的地理隐私，同时通过促进和促进地理空间数据共享来使研究社区受益。