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.
翻译:在COVID-19大流行期间,收集了大量各类数据,这些数据的分析和解释对于遏制冠状病毒的传播是必不可少的,随着该大流行缓慢,该大流行期间收集的数据将继续是进一步研究该大流行和了解其对公共卫生、经济和社会的影响的丰富来源。另一方面,公布和分享信息可与严重的隐私关切相联系。在本文件中,旨在阐明隐私保护共享大流行数据的情况,从而促进和鼓励更多数据共享,供研究和公开使用。我们研究了在大流行期间收集的三种共同数据类型 -- -- 病例监测、病人定位历史和热点地图,以及联系追踪网络 -- -- 并制订和采用隐私保护办法,公布或分享每一种数据类型。我们举例说明了各种应用情况,并在各种隐私保障的范例和实验中审查了释放的隐私保护数据的效用。