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Encounter Metrics and Exposure Notification

Published

Author(s)

Rene Peralta, Angela Robinson

Abstract

We discuss the measurement of aggregate levels of encounters in a population, a concept we call encounter metrics. Encounter metrics are designed so that they can be deployed while preserving the privacy of individuals. To this end, encounters are labeled with a random number that cannot be linked to anything that is broadcast at the time of the encounter. Among the applications of encounter metrics is privacy-preserving exposure notifcation, a system that allows people to obtain a measure of their risk due to past encounters with people who have self-reported to be positive with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-19), the cause of coronavirus disease 2019 (COVID-19). The precise engineering of a system for exposure notifcation should be targeted to particular environments. We outline a system for use in the context of a workplace such as the National Institute of Standards and Technology (NIST).
Citation
Journal of Research (NIST JRES) -
Volume
126

Keywords

contact tracing, COVID-19, Diffe-Hellman, elliptic curves, encounter metrics, exposure notifcation, privacy-enhancing cryptography

Citation

Peralta, R. and Robinson, A. (2021), Encounter Metrics and Exposure Notification, Journal of Research (NIST JRES), National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/jres.126.003, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=931029 (Accessed September 19, 2021)
Created March 28, 2021, Updated May 14, 2021