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Analytically Valid Discrete Microdata Files and Re-identification

William E. Winkler

KEY WORDS: Data Quality, Loglinear Model Fit, Missing Data, Convex Constraints

ABSTRACT

Loglinear modeling methods have become quite straightforward to apply to discrete data X. A good-fitting loglinear model can be used to generate synthetic copies of X1, …, Xn of X that preserve analytic properties but may allow reidentification of small cells. With fitting algorithms that use more general convex constraints and are designed to deal with missing data, we are able to disperse the counts associated with small cells over other cells in a manner that reduces reidentification risk while still maintaining most analytic properties.

CITATION:

Source: U.S. Census Bureau, Statistical Research Division

Created: December 10, 2007
Last revised: December 10, 2007


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Source: U.S. Census Bureau | Statistical Research Division | (301) 763-3215 (or chad.eric.russell@census.gov) |   Last Revised: October 08, 2010