Dataset Information

Street masking: a network-based geographic mask for easily protecting geoprivacy.

ABSTRACT:

Background

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.

Results

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.

Conclusion

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.

SUBMITTER: Swanlund D

PROVIDER: S-EPMC7336090 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Face masks are now seen as a key tool in the world's recovery from the COVID-19 pandemic. However, during the early stages of the outbreak, face mask use in the United Kingdom (UK) was significantly lower than that of countries equally impacted by the virus. We were interested to explore whether social cognitions played a role in levels of mask wearing. A cross-sectional online survey of UK adults (n=908) was conducted in July 2020. Estimated face mask use and thoughts about wearing face masks were assessed using measures developed for this study. Participants also answered questions about their general mood, social anxiety and basic demographic data. Multiple regression was used to examine factors associated with mask wearing. Participants' estimated mask wearing was low when in public spaces, such as the park (17%) or walking on the high street (36%). However, broadly fitting with UK guidance at the time, rates were considerably higher when in situations of closer proximity to others, such as on public transport (94%), in a shop or café (62%), when speaking to somebody in an enclosed public space (67%) or in a busy area when social distancing was not possible (79%). When looking at estimated mask wearing when in proximity to others, positive social cognitions (e.g., I'll look confident and competent wearing a mask) were associated with more wearing, whereas negative social cognitions (e.g., I'll look anxious, I'll look foolish) were associated with less wearing. These results remained after controlling for factors that have indicated increased risk from COVID-19 (age, gender, ethnicity, presence of a health condition or pregnancy), belief about the health benefit for others and levels of depression and social anxiety. The largest predictors of mask wearing were the amount of people believed wearing a mask would keep others safe and the presence of an underlying health condition. The study findings indicate that future public health campaigns would benefit from a focus on strengthening beliefs about the protective benefits of masks, but also promoting positive social messages about wearing in public (e.g., mask wearing means you are confident and competent).