Some Examples of Privacy-preserving Publication and Sharing of COVID-19
Pandemic Data
- URL: http://arxiv.org/abs/2106.10339v3
- Date: Tue, 13 Sep 2022 21:49:26 GMT
- Title: Some Examples of Privacy-preserving Publication and Sharing of COVID-19
Pandemic Data
- Authors: Fang Liu, Dong Wang, Tian Yan
- Abstract summary: We use three common but distinct data types collected during the pandemic to illustrate the publication and sharing of granular information and individual-level pandemic data in a privacy-preserving manner.
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.
- Score: 9.514015456352265
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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, na\"{i}ve 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.
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