Daily activity locations k-anonymity for the evaluation of disclosure risk of individual GPS datasets
Daily activity locations k-anonymity for the evaluation of disclosure risk of individual GPS datasets
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DOI：
10.1186/s12942-020-00201-9
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发表时间：
2020-03-05
影响因子：
4.9
通讯作者：
Kwan, Mei-Po
中科院分区：
文献类型：
--
作者：
Wang, Jue;Kwan, Mei-Po
背景个人隐私是大数据时代的一个重要问题。在健康地理学领域，个人健康数据的收集伴随着地理位置信息，这可能会增加披露风险并威胁到个人隐私。地理伪装通过掩盖地理位置信息来保护个人隐私，空间k-匿名被广泛用于度量地理伪装后的信息泄露风险。随着包含大量机密地理空间信息的个人GPS轨迹数据集的出现，披露风险不再能够通过空间k-匿名方法进行全面评估。方法本研究提出并发展了每日活动位置（DAL）k-匿名作为一种新的方法来评估GPS数据的披露风险。而不是仅基于一个地理位置计算披露风险（例如，新的DAL k-匿名是基于个人的所有活动位置以及他/她在从GPS数据集提取的每个位置花费的时间对披露风险的综合评估。通过模拟的个人GPS数据集，我们提出了在各种情况下应用DAL k-匿名的案例研究，以研究其性能。应用DAL k-匿名的结果也与空间k-匿名在这些情况下得到的结果进行了比较。结果研究结果表明，DAL k-匿名提供了一个更好的估计披露风险比空间k-匿名。在个人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个人的家和所有其他日常活动位置的概率，提供了一种更有效的评估披露风险的方法。结论这种新方法为了解共享或发布GPS数据的披露风险提供了一种定量手段。它还有助于为GPS数据集开发新的geomasking方法。最终，这项研究的结果将有助于保护个人隐私，同时通过促进和促进地理空间数据共享使研究界受益。
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.