Can You Fake It Until You Make It? Impacts of Differentially Private Synthetic Data on Downstream Classification Fairness

Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
Natalie Dullerud
Natalie Dullerud

Natalie Dullerud is a second-year M.S. student at University of Toronto, co-supervised by Dr. Marzyeh Ghassemi and Dr. Nicolas Papernot. Her research broadly focuses on various facets of fair and equitable machine learning; intersections between conceptualizations of privacy and machine learning; and applications in medical and biological contexts. Previously, she completed her B.S. in Mathematics at University of Southern California.