Privacy Preservation in Epidemic Data Collection

TL;DR

This paper proposes privacy-preserving methods for epidemic data collection using voluntary smartphone apps to track population density, contact tracing, infection locations, and disease spread timelines, balancing privacy with data accuracy.

Contribution

It introduces novel privacy-preserving techniques for epidemic data collection that enable accurate analysis while respecting individual privacy and voluntary participation.

Findings

Methods enable privacy-preserving population density estimation.

Contact tracing can be performed without compromising privacy.

Data accuracy improves with higher participation rates.

Abstract

This work is inspired by the outbreak of COVID-19, and some of the challenges we have observed with gathering data about the disease. To this end, we aim to help collect data about citizens and the disease without risking the privacy of individuals. Specifically, we focus on how to determine the density of the population across the country, how to trace contact between citizens, how to determine the location of infections, and how to determine the timeline of the spread of the disease. Our proposed methods are privacy-preserving and rely on an app to be voluntarily installed on citizens' smartphones. Thus, any individual can choose not to participate. However, the accurateness of the methods relies on the participation of a large percentage of the population.