Some Examples of Privacy-preserving Publication and Sharing of COVID-19 Pandemic Data
This addresses privacy concerns for public health researchers and policymakers sharing pandemic data, but it is incremental as it builds on existing differential privacy methods.
The study tackled the problem of privacy risks in sharing granular COVID-19 data by applying differential privacy to three data types, demonstrating through simulations and real data that privacy-preserving data release is feasible while maintaining statistical utility.
A considerable amount of various types of data have been collected during the COVID-19 pandemic, the analysis and interpretation of which have been indispensable for curbing the spread of the disease. As the pandemic moves to an endemic state, the data collected during the pandemic will continue to be rich sources for further studying and understanding the impacts of the pandemic on various aspects of our society. On the other hand, naïve release and sharing of the information can be associated with serious privacy concerns. In this study, we use three common but distinct data types collected during the pandemic (case surveillance tabular data, case location data, and contact tracing networks) to illustrate the publication and sharing of granular information and individual-level pandemic data in a privacy-preserving manner. We leverage and build upon the concept of differential privacy to generate and release privacy-preserving data for each data type. We investigate the inferential utility of privacy-preserving information through simulation studies at different levels of privacy guarantees and demonstrate the approaches in real-life data. All the approaches employed in the study are straightforward to apply. Our study generates statistical evidence on the practical feasibility of sharing pandemic data with privacy guarantees and on how to balance the statistical utility of released information during this process.