ArticleScience advances2024
Evaluating bias and noise induced by the U.S. Census Bureau's privacy protection methods.
Article in Science advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- The 2020 US Decennial Census is more private than you (might) think.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- The shortcomings of synthetic census microdata.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Privacy violations in election results.Science advances · 2025Article
- School desegregation by redrawing district boundaries.Scientific reports · 2024Article
- Census officials must constructively engage with independent evaluations.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The U.S. Census Bureau faces a difficult trade-off between the accuracy of Census statistics and the protection of individual information. We conduct an independent evaluation of bias and noise induced by the Bureau's two main disclosure avoidance systems: the TopDown algorithm used for the 2020 Census and the swapping algorithm implemented for the three previous Censuses. Our evaluation leverages the Noisy Measurement File (NMF) as well as two independent runs of the TopDown algorithm applied to the 2010 decennial Census. We find that the NMF contains too much noise to be directly useful without measurement error modeling, especially for Hispanic and multiracial populations. TopDown's postprocessing reduces the NMF noise and produces data whose accuracy is similar to that of swapping. While the estimated errors for both TopDown and swapping algorithms are generally no greater than other sources of Census error, they can be relatively substantial for geographies with small total populations.
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