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The United States Census Bureau faces a difficult trade-off between theaccuracy of Census statistics and the protection of individual information. Weconduct the first independent evaluation of bias and noise induced by theBureau's two main disclosure avoidance systems: the TopDown algorithm employedfor the 2020 Census and the swapping algorithm implemented for the threeprevious Censuses. Our evaluation leverages the Noisy Measure File (NMF) aswell as two independent runs of the TopDown algorithm applied to the 2010decennial Census. We find that the NMF contains too much noise to be directlyuseful, especially for Hispanic and multiracial populations. TopDown'spost-processing dramatically reduces the NMF noise and produces data whoseaccuracy is similar to that of swapping. While the estimated errors for bothTopDown and swapping algorithms are generally no greater than other sources ofCensus error, they can be relatively substantial for geographies with smalltotal populations.

Evaluating Bias and Noise Induced by the U.S. Census Bureau's Privacy Protection Methods