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

arXiv: https://arxiv.org/abs/2212.04133 · HTML: https://mlsanity.com/paper/2212.04133/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2022.

## Built on by (most cited first, 4 of 4)

- [DPack: Efficiency-Oriented Privacy Budget Scheduling](https://mlsanity.com/paper/2212.13228/) (2022)
- [Qrlew: Rewriting SQL into Differentially Private SQL](https://mlsanity.com/paper/2401.06273/) (2024)
- [Differentially Private Modeling of Disease Transmission within Human Contact Networks](https://mlsanity.com/paper/2604.07493/) (2026)
- [Predictability as a Fine-Grained Measure for Privacy](https://mlsanity.com/paper/2606.20546/) (2026)

## Abstract (arXiv)

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.