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Encounter Metrics and Exposure Notification



Rene Peralta, Angela Robinson


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).
Journal of Research (NIST JRES) -


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


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],, (Accessed July 19, 2024)


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Created March 28, 2021, Updated May 14, 2021