Privacy-preserving Publication and Sharing of COVID-19 Pandemic Data

Wang, Dong; Liu, Fang

A huge amount of data of various types are collected during the COVID-19 pandemic, the analysis and interpretation of which has been indispensable for curbing the spread of the coronavirus. As the pandemic slows down, the collected data during the pandemic will continue to be rich sources for further studying the pandemic and understanding its impacts on public health, economics, and societies. On the other hand, na\"{i}ve release and sharing of the information can be associated with serious privacy concerns. In this paper, aiming at shedding light on privacy-preserving sharing of pandemic data and thus promoting and encouraging more data sharing for research and public use, we examine three common data types -- case surveillance, patient location histories and hot spot maps, and contact tracing networks -- collected during the pandemic and develop and apply privacy-preserving approaches for publishing or sharing each data type. We illustrate the applications and examine the utility of released privacy-preserving data in examples and experiments at various levels of privacy guarantees.