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As the use of wastewater for public health surveillance continues to expand, inevitably sample collection will move from centralized wastewater treatment plants to sample collection points within the sewer collection system to isolate individual neighborhoods and communities. Collecting data at this geospatial resolution will help identify variation in select biomarkers within neighborhoods, ultimately making the wastewater-derived data more actionable. However a challenge in achieving this is the nature of the wastewater collection system, which aggregates and commingles wastewater from various municipalities. Thus various stakeholders from different cities must collectively provide information to separate wastewater catchments to achieve neighborhood-specific public health information. Data sharing restrictions and the need for anonymity complicates this process.
This talk presents our approaches to enabling data privacy in wastewater-based epidemiology. Our methodology is built upon a cryptographic technique, Homomorphic Encryption (HE), ensuring privacy. Additionally, we outline a technique to enhance the performance of HE, which could be of independent interest.
Speaker Bio:
Ni Trieu is currently an Assistant Professor at Arizona State University (ASU). Her research interests lie in the area of cryptography and security, with a specific focus on secure computation and its applications such as private set intersection, private database queries, and privacy-preserving machine learning. Prior to joining ASU, she was a postdoc at UC Berkeley. She received her Ph.D. degree from Oregon State University.
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