Distribution-Preserving Statistical Disclosure Limitation

Simon D. Woodcock () and Gary Benedetto ()
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Gary Benedetto: US Census Bureau

Discussion Papers from Department of Economics, Simon Fraser University

Abstract: One approach to limiting disclosure risk in public-use microdata is to release multiply-imputed, partially synthetic data sets. These are data on actual respondents, but with con dential data replaced by multiply-imputed synthetic values. When imputing confidential values, a mis-specified model can invalidate inferences, because the distribution of synthetic data is determined by the model used to generate them. We present a practical method to generate synthetic values when the imputer has only limited information about the true data generating process. We combine a simple imputation model (such as regression) with a series of density-based transformations to pre- serve the distribution of the con dential data, up to sampling error, on speci ed subdomains. We demonstrate through simulation and a large scale application that our approach preserves important statistical properties of the con dential data, including higher moments, with low disclosure risk.

Keywords: statistical disclosure limitation; confidentiality; privacy; multiple imputation; partially synthetic data (search for similar items in EconPapers)
JEL-codes: C1 C4 C5 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm
Date: 2007-09
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Related works:
Journal Article: Distribution-preserving statistical disclosure limitation (2009)
Working Paper: Distribution Preserving Statistical Disclosure Limitation (2006)
Working Paper: Distribution-Preserving Statistical Disclosure Limitation (2006)
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