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MaskMyXYZ: An easy-to-use platform for anonymizing spatial health data

Authors: Mariana Brussoni, University of British Columbia, David Swanlund*, Simon Fraser University, Nadine Schuurman, Simon Fraser University
Topics: Geographic Information Science and Systems, Hazards and Vulnerability
Keywords: privacy, gis, anonymization
Session Type: Paper
Presentation File: No File Uploaded


Recent research has demonstrated that sensitive health data is routinely disclosed in maps published in academic articles (Haley et al., 2016; Kounadi & Leitner, 2014). These privacy violations can easily be avoided by using geographic masks, which are techniques that allow sensitive spatial data to be published while also protecting participant privacy. Despite nearly two decades of research into geographic masks, however, adoption remains limited. While several reasons may be behind this, the lack of clear guidance and usable tools for executing geographic masks represent significant barriers. This presentation will describe an open source web application that aims to increase the adoption of geographic masks. It uses JavaScript GIS libraries, such as Turf.js, to perform donut geomasking to point data in mere seconds, and includes metrics describing both privacy protection and information loss. A client-side architecture ensures the confidentiality of the user’s data while eliminating the friction of installing third-party tools. Moreover, an easy-to-use, interactive web interface helps guide and educate users about geographic masking. Together these features facilitate the adoption of geographic masks such that privacy can be better protected in health research.

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