CICI:UCSS:Improving the Privacy and Security of Data for Wastewater-based Epidemiology

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The field of Wastewater-Based Epidemiology (WBE) collects and analyzes data from sewage systems that relate to public health. These data include genetic and chemical biomarkers of identity, ethnicity, behavior, consumption, pollution, and pathogenic infections. Interest in WBE exploded recently, as researchers turned to wastewater samples to estimate SARS-CoV-2 infection levels in local populations and inform public health responses to the pandemic. Because data collected from wastewater are aggregated at the population level, they are typically assumed to be anonymous, and are therefore not subject to health privacy or other regulatory protections. These data are most useful when shared, yet sharing raises security and privacy issues, for individuals as well as neighborhoods, schools and governments. The project is developing technology and tools to support legitimate uses of WBE datasets while minimizing abuse, and it is designing protocols for protecting individual and organizational privacy. These tools will encourage wide participation in WBE consortia by enabling organizations to protect their data from adverse uses, which in turn will help improve the health of the general public and reduce morbidity and mortality.The project addresses three key security threats: those posed by individual queries; those posed by sharing, whether through joint queries or large-scale association studies; and those posed by the presence of human genetic material in wastewater samples. These threats are mitigated by: (1) supporting secure queries on wastewaster data using homomorphic encryption; (2) enabling federated data analysis based on secure computation; and (3) protecting genomic privacy with differential privacy. These components are being implemented to allow different entities to execute query processing and train Machine Learning models on joint databases while maintaining the privacy and format of each database. The system is being evaluated using real-world datasets with use cases that match important threat scenarios.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.