Geographic 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. Street 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. Street 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.

中文翻译：

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街道遮罩：基于网络的地理遮罩，可轻松保护地理隐私。

地理遮罩是用于保护已发布地图中个人隐私的技术，但在研究中使用率极低。由于敏感的健康记录在未遮盖的地图中处于危险之中，因此会导致持续侵犯个人隐私。需要新的地理掩蔽方法，以促进可访问性和易用性，以便使其更广泛地被采用。本文介绍了一种称为街道屏蔽的新地理屏蔽方法，该方法通过自动检索OpenStreetMap数据并使用道路网络作为屏蔽的基础，减轻了用户查找补充人口数据的负担。我们将其与考虑了人口密度和不考虑人口密度的甜甜圈地理掩蔽进行了比较，以评估其对需要略少一些补充数据的地理掩膜的功效。我们的分析是对三个加拿大城市的综合数据进行的。在隐私保护方面，街道遮罩的执行方式与基于人口的甜甜圈地理遮罩相似，在相似的中位位移距离处可获得可比的k-匿名值。不出所料，基于距离的甜甜圈地理遮罩在隐私保护方面表现最差。街道掩膜在信息丢失方面也表现出色，与基于人口的甜甜圈地掩膜相比，实现了更好的集群保存和土地覆盖协议。基于距离的甜甜圈地理遮罩与街道遮罩类似，但以减少隐私保护为代价。街道遮罩可以与基于人口的甜甜圈地理遮罩（如果不能胜过）竞争，并且不需要用户提供任何补充数据。而且，与大多数其他地理遮罩不同，它极大地降低了错误归因的风险，并固有地考虑了许多地理障碍。Python用户可以轻松访问它，并为非编码用户构建接口提供了基础，从而可以在敏感的地理空间研究中更好地保​​护隐私。