  Researchers from a growing range of fields and industries rely on
public-access census data. These data are altered by census-taking agencies to
minimize the risk of identification; one such disclosure avoidance measure is
the data swapping procedure. I study the effects of data swapping on
contingency tables using a dummy dataset, public-use American Community Survey
(ACS) data, and restricted-use ACS data accessed within the U.S.\ Census
Bureau. These simulations demonstrate that as the rate of swapping is varied,
the effect on joint distributions of categorical variables is no longer
understandable when the data swapping procedure attempts to target at-risk
individuals for swapping using a simple targeting criterion.
