RESUMO
Population-scale COVID-19 management benefits from timely and honest information from billions of people. Here, we provide a first report on the FeverIQ symptom tracker, a global effort to collect symptom and test data which has received more than 3.6 million submissions. Unlike other trackers, FeverIQ uses secure multiparty computation (SMC) to cryptographically guarantee user privacy while providing insights to scientists and public health efforts. We performed basic integrity checks of the FeverIQ dataset, such as by comparing it to other publicly released data. We then trained a linear classifier on diagnosis scores which were computed securely, without unprotected symptom data ever leaving a users phone or computer. FeverIQ is currently the worlds largest application of SMC in a health context, demonstrating the practicality of privacy-preserving analytics for population-scale digital health interventions.
