Spatial K-anonymity: A Privacy-preserving Method for COVID-19 Related Geospatial Technologies

There is a growing need for spatial privacy considerations in the many\ngeo-spatial technologies that have been created as solutions for\nCOVID-19-related issues. Although effective geo-spatial technologies have\nalready been rolled out, most have significantly sacrificed privacy for\nutility. In this paper, we explore spatial k-anonymity, a privacy-preserving\nmethod that can address this unnecessary tradeoff by providing the best of both\nprivacy and utility. After evaluating its past implications in geo-spatial use\ncases, we propose applications of spatial k-anonymity in the data sharing and\nmanaging of COVID-19 contact tracing technologies as well as heat maps showing\na user's travel history. We then justify our propositions by comparing spatial\nk-anonymity with several other spatial privacy methods, including differential\nprivacy, geo-indistinguishability, and manual consent based redaction. Our hope\nis to raise awareness of the ever-growing risks associated with spatial privacy\nand how they can be solved with Spatial K-anonymity.\n