  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.
