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Spatial K-anonymity: A Privacy-preserving Method for COVID-19 Related Geospatial Technologies
- Publication Year :
- 2021
-
Abstract
- 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.
- Subjects :
- Computer Science - Cryptography and Security
Subjects
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2101.02556
- Document Type :
- Working Paper