Department of Geography, University of Toronto Mississauga, 3359 Mississauga Road, Mississauga, ON, L5L 1C6, Canada.
Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, Hong Kong, China.
Int J Health Geogr. 2020 Mar 5;19(1):7. doi: 10.1186/s12942-020-00201-9.
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.
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.
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.
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.
在大数据时代，个人隐私是一个重要的关注点。在健康地理学领域，个人健康数据与地理位置信息一起被收集，这可能会增加泄露风险并威胁个人地缘隐私。地理掩蔽技术被用来保护个人的地缘隐私，通过掩蔽地理位置信息，空间 k-匿名被广泛用于衡量地理掩蔽后的数据泄露风险。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，空间 k-匿名方法已经不能全面评估泄露风险。
本研究提出并开发了日常活动地点（DAL）k-匿名法，作为评估 GPS 数据泄露风险的新方法。新的 DAL k-匿名法不是基于个人的一个地理位置（例如，家庭）来计算泄露风险，而是基于从 GPS 数据集中提取的个人所有活动地点及其在每个地点停留的时间的综合评估。通过模拟个人 GPS 数据集，我们展示了在各种场景中应用 DAL k-匿名法的案例研究，以调查其性能。还在这些场景下比较了 DAL k-匿名法和空间 k-匿名法的结果。
本研究结果表明，DAL k-匿名法比空间 k-匿名法提供了更好的泄露风险估计。在个人 GPS 数据的各种案例研究场景中，DAL k-匿名法通过考虑重新识别个人家庭和所有其他日常活动地点的概率，提供了一种更有效的评估泄露风险的方法。
这种新方法为理解共享或发布 GPS 数据的泄露风险提供了一种定量手段。它还为开发新的 GPS 数据集地理掩蔽方法提供了新的思路。最终，本研究的发现将有助于保护个人地缘隐私，同时通过促进和便利地理空间数据共享，使研究社区受益。
