CISE-MSI: RCBP-RF: SaTC: Privacy Preserving Models Leveraging Mobility Data for Public Health

Florida Agricultural And Mechanical University, Tallahassee FL

Investigators

Abstract

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). The use of health applications on mobile devices is becoming increasingly popular. With that popularity comes a desire to use the mobility data that is generated for various public health purposes, such as contact tracing during COVID-19. It is also used in more complex applications that use machine learning to infer health risks. On one hand, these models promise a transformative impact on targeted public health interventions. On the other hand, results from these models could compromise the privacy of an individual’s health status without directly using health data. Even when the mobility data is de-identified, privacy can be compromised when physical observations of persons’ locations augment the models’ results. People who are considered visible minorities are particularly vulnerable if they come from groups with a disproportionate prevalence of a certain disease. There are ways to adjust privacy techniques that can help mitigate privacy risks, however, they could compromise the accuracy of the models. There is a need for solutions that can yield effective public health models while preserving privacy. Results from this project will be the development of infection spread models that can do just that – give accurate place-based data without compromising privacy for health related applications. The project will use a establish and understanding of effective approaches for co-designing privacy and security techniques with infection spread modeling. These privacy protection approaches would account for potential compromise through physical observations in combination with queries to the models. We will also produce a synthetic population for Northwest Florida designed for efficient updates through data assimilation. Such synthetic-data-driven models have the potential to yield accurate results while preserving privacy. The impact on research and education will be seen in the developing of research capacity at FAMU as well as through interdisciplinary research tasks to be conducted by undergraduate students that are traditionally underrepresented in computing. Results from this project will help expand the pathways into computing fields and other interdisciplinary careers This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.