English

Tumult Analytics: a robust, easy-to-use, scalable, and expressive framework for differential privacy

Cryptography and Security 2022-12-09 v1

Abstract

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.

Keywords

Cite

@article{arxiv.2212.04133,
  title  = {Tumult Analytics: a robust, easy-to-use, scalable, and expressive framework for differential privacy},
  author = {Skye Berghel and Philip Bohannon and Damien Desfontaines and Charles Estes and Sam Haney and Luke Hartman and Michael Hay and Ashwin Machanavajjhala and Tom Magerlein and Gerome Miklau and Amritha Pai and William Sexton and Ruchit Shrestha},
  journal= {arXiv preprint arXiv:2212.04133},
  year   = {2022}
}
R2 v1 2026-06-28T07:25:36.976Z