摘要 : 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 en... 展开 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). 收起