Spatial K-anonymity: A Privacy-preserving Method for COVID-19 Related Geospatial Technologies
This paper tackles the problem of privacy leakage in geospatial COVID-19 applications for individuals, proposing a known method for a new application area.
The paper addresses the privacy concerns in COVID-19 related geospatial technologies, which often sacrifice privacy for utility. It proposes spatial k-anonymity as a privacy-preserving method to balance both privacy and utility in applications like contact tracing and travel history heat maps.
There is a growing need for spatial privacy considerations in the many geo-spatial technologies that have been created as solutions for COVID-19-related issues. Although effective geo-spatial technologies have already been rolled out, most have significantly sacrificed privacy for utility. In this paper, we explore spatial k-anonymity, a privacy-preserving method that can address this unnecessary tradeoff by providing the best of both privacy and utility. After evaluating its past implications in geo-spatial use cases, we propose applications of spatial k-anonymity in the data sharing and managing of COVID-19 contact tracing technologies as well as heat maps showing a user's travel history. We then justify our propositions by comparing spatial k-anonymity with several other spatial privacy methods, including differential privacy, geo-indistinguishability, and manual consent based redaction. Our hope is to raise awareness of the ever-growing risks associated with spatial privacy and how they can be solved with Spatial K-anonymity.