Curtis Mitchell, xD, United States Census Bureau
The ability to work with genomic datasets across institutions is a promising approach to understanding and treating diseases such as rare cancers. However, the sharing of genomic data raises challenging legal and ethical concerns around patient privacy. In this talk we will present on ongoing work between the National Institute of Standards and Technology (NIST), the US Census Bureau, and other organizations to explore metrics and use cases for privacy-preserving machine learning on genomic data. We will discuss the goals of the project, the technical architecture of the project using privacy-preserving federated learning, and the initial results on performance and privacy metrics we have obtained using plant genomic data as an initial stand-in for human genomic data.
Additional authors: Gary Howarth and Justin Wagner, NIST; Jess Stahl, Census; Christine Task and Karan Bhagat, Knexus; Amy Hilla and Rebecca Steinberg, MITRE
Curtis Mitchell is an Emerging Technology Fellow on the xD team at the US Census Bureau where he is contributing to a variety of projects involving privacy-enhancing technologies, artificial intelligence, and modern web applications. He has over 15 years of experience in software- and data-related roles at small startups, large corporations, and open-source communities. Prior to joining the Census Bureau, he worked at NASA's Ames Research Center.
author = {Curtis Mitchell},
title = {Establishing Privacy Metrics for Genomic Data Analysis},
year = {2025},
address = {Santa Clara, CA},
publisher = {USENIX Association},
month = jun
}
