Dynamically Adjusting Case-Reporting Policy to Maximize Privacy and Utility in the Face of a Pandemic

Managing a pandemic requires continuous dissemination of infectious disease surveillance data. Legislation permits sharing de-identified patient data; however, current de-identification approaches are time-consuming and do not flex with changes in infection rates or population demographics over time. In this paper, we introduce a framework to dynamically adapt de-identification for near-real time sharing of patient-level surveillance data. The framework leverages a simulation mechanism, capable of being applied to any geographic level, to forecast and manage disclosure risks. We use data from Johns Hopkins University and the Centers for Disease Control and Prevention to demonstrate the framework's effectiveness in maintaining the privacy risk below a threshold based on public health standards for COVID-19 county-level case data from August 2020 to April 2021. Across all US counties, the framework's approach meets the threshold for 95.2% of daily data releases, while a policy based on current de-identification techniques meets the threshold for only 24.6%.