|
Abstract:
|
Strong anonymization techniques, like differential privacy, can be used to publish data and help researchers and public health authorities understand and combat the spread of COVID-19. In the Community Mobility Reports, we generate a set of anonymized metrics from the data of Google users who opted in to Location History, and publish percentage changes of these metrics from a historical baseline. In the Search Trends symptoms dataset, we aggregate and anonymize trends in Google searches for symptoms and other related topics.
First, we outline the anonymization process used for both projects. We then explain how we limit the impact of adding noise on the reliability of the metrics, and how we can continuously make improvements to the underlying computation of the metrics without incurring large additional costs in the privacy budget. Finally, we give a few examples of usage of this data by researchers and public health authorities in their efforts to combat the COVID-19 epidemic.
|
