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

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arxiv 2212.04133 v1 pith:C72U5NK3 submitted 2022-12-08 cs.CR

Skye Berghel , Philip Bohannon , Damien Desfontaines , Charles Estes , Sam Haney , Luke Hartman , Michael Hay , Ashwin Machanavajjhala
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Tom Magerlein Gerome Miklau Amritha Pai William Sexton Ruchit Shrestha
This is my paper · ORCID
classification cs.CR
keywords analyticsdifferentialframeworkprivacytumultbureaucensusdesign
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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