Abstract
BackgroundGeographic masks are techniques used to protect individual privacy in published maps but are highly under-utilized in research. This leads to continual violations of individual privacy, as sensitive health records are put at risk in unmasked maps. New approaches to geographic masking are required that foster accessibility and ease of use, such that they become more widely adopted. This article describes a new geographic masking method, called street masking, that reduces the burden on users of finding supplemental population data by instead automatically retrieving OpenStreetMap data and using the road network as a basis for masking. We compare it to donut geomasking, both with and without population density taken into account, to evaluate its efficacy against geographic masks that require slightly less and slightly more supplemental data. Our analysis is performed on synthetic data in three different Canadian cities.ResultsStreet masking performs similarly to population-based donut geomasking with regard to privacy protection, achieving comparable k-anonymity values at similar median displacement distances. As expected, distance-based donut geomasking performs worst at privacy protection. Street masking also performs very well regarding information loss, achieving far better cluster preservation and landcover agreement than population-based donut geomasking. Distance-based donut geomasking performs similarly to street masking, though at the cost of reduced privacy protection.ConclusionStreet masking competes with, if not out-performs population-based donut geomasking and does so without requiring any supplemental data from users. Moreover, unlike most other geographic masks, it significantly minimizes the risk of false attribution and inherently takes many geographic barriers into account. It is easily accessible for Python users and provides the foundation for interfaces to be built for non-coding users, such that privacy can be better protected in sensitive geospatial research.
Highlights
Geographic masks are techniques used to protect individual privacy in published maps but are highly under-utilized in research
Results (Table 1) indicate that regarding privacy protection, measured using k-anonymity, street masking performs relatively on par with both population- and distance-based donut geomasking in Vancouver
While median displacement distance was used as a control between masks, average displacement distance was highest for population-based donut geomasking, and lowest for distance-based donut geomasking across all masking variations
Summary
Geographic masks are techniques used to protect individual privacy in published maps but are highly under-utilized in research. This article describes a new geographic mask called street masking that uses a highly novel, networkbased approach to allow researchers to quickly, and robustly protect privacy in their maps. On the other hand, could include translating, rotating, or scaling point patterns either globally (e.g. transforming the entire point pattern at once) or locally (i.e. dividing the point pattern based on a grid and transforming each cell differently) Both of these masks suffer from critical weaknesses: with random perturbation it is entirely possible that some points will only be moved 1 m, and will not adequately protect privacy, while for affine transformations if an attacker knows the identity of only a few points it becomes possible to reidentify the entire point pattern [2, 12]
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