Tumult Analytics: a robust, easy-to-use, scalable, and expressive framework for differential privacy
Skye Berghel, Philip Bohannon, Damien Desfontaines, Charles Estes, Sam Haney, Luke Hartman, Michael P. Hay, Ashwin Machanavajjhala, Tom Magerlein, Gerome Miklau, Amritha Pai, William Sexton, Ruchit Shrestha · arXiv (Cornell University) · 2022
In this short paper, we outline the design of Tumult Analytics, a Python framework for differential privacy used at institutions such as the U.S. Census Bureau, the Wikimedia Foundation, or the Internal Revenue Service.