Privacy Preservation in Epidemic Data Collection
It addresses privacy concerns in public health data collection during pandemics, but is incremental as it builds on existing privacy-preserving and app-based approaches.
The paper tackles the problem of collecting epidemic data like COVID-19 spread while preserving individual privacy, using a voluntary smartphone app to estimate population density, contact tracing, infection locations, and disease timeline, with accuracy dependent on high participation rates.
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.