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The 2020 Census Disclosure Avoidance System TopDown Algorithm

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arxiv 2204.08986 v1 pith:R4UC6LB4 submitted 2022-04-19 cs.CR econ.EMstat.AP

classification cs.CRecon.EMstat.AP
keywords censusalgorithmdatasystemaccountingavoidancedifferentialdisclosure
verification ladder T0 review T1 audit T2 compute T3 formal

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The Census TopDown Algorithm (TDA) is a disclosure avoidance system using differential privacy for privacy-loss accounting. The algorithm ingests the final, edited version of the 2020 Census data and the final tabulation geographic definitions. The algorithm then creates noisy versions of key queries on the data, referred to as measurements, using zero-Concentrated Differential Privacy. Another key aspect of the TDA are invariants, statistics that the Census Bureau has determined, as matter of policy, to exclude from the privacy-loss accounting. The TDA post-processes the measurements together with the invariants to produce a Microdata Detail File (MDF) that contains one record for each person and one record for each housing unit enumerated in the 2020 Census. The MDF is passed to the 2020 Census tabulation system to produce the 2020 Census Redistricting Data (P.L. 94-171) Summary File. This paper describes the mathematics and testing of the TDA for this purpose.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Fingerprinting Codes Meet Geometry: Improved Lower Bounds for Private Query Release and Adaptive Data Analysis

    cs.DS 2024-12 reject novelty 8.0 of 10

    The geometric fingerprinting framework yields new lower bounds for adaptive data analysis and random queries, but the claimed log(1/delta) matching bound for private query release is not established by the proof.

  2. Do You Really Need Public Data? Surrogate Public Data for Differential Privacy on Tabular Data

    cs.LG 2025-04 conditional novelty 6.0 of 10

    LLM-generated surrogate public data, built from schema metadata alone, can substitute for traditional public data when pretraining differentially private tabular classifiers in small-data settings.

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