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Tumult Analytics: a robust, easy-to-use, scalable, and expressive framework for differential privacy
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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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Cited by 3 Pith papers
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SafeTab-P: Disclosure Avoidance for the 2020 Census Detailed Demographic and Housing Characteristics File A (Detailed DHC-A)
SafeTab-P adds discrete Gaussian noise and adaptively chooses how detailed each census table is, with a proof of zero-concentrated differential privacy for the 2020 Detailed DHC-A file.
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PHSafe: Disclosure Avoidance for the 2020 Census Supplemental Demographic and Housing Characteristics File (S-DHC)
PHSafe, the privacy algorithm behind the 2020 Census S-DHC tables, is described with a zCDP proof based on a private join with truncation.
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SafeTab-H: Disclosure Avoidance for the 2020 Census Detailed Demographic and Housing Characteristics File B (Detailed DHC-B)
SafeTab-H adds discrete Gaussian noise to household counts under a formal differential-privacy guarantee, and this paper provides the algorithm description, privacy proof, and parameter selection for the 2020 Detailed...
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