mlsanityData to 7 Oct 2026

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

arXiv 2212.04133 ↗First posted 8 Dec 2022cs.CRcited by 4

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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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Dec 2022.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 0; 2026: 2.

4 papers.

  1. 2022DPack: Efficiency-Oriented Privacy Budget Schedulingcited by 1
  2. 2024Qrlew: Rewriting SQL into Differentially Private SQLcited by 1
  3. 2026Differentially Private Modeling of Disease Transmission within Human Contact Networkscited by 0
  4. 2026Predictability as a Fine-Grained Measure for Privacycited by 0