Distribution-preserving statistical disclosure limitation

Simon Woodcock and Gary Benedetto

Computational Statistics & Data Analysis, 2009, vol. 53, issue 12, 4228-4242

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 confidential data replaced by multiply-imputed synthetic values. A mis-specified imputation model can invalidate inferences based on the partially synthetic data, because the imputation model determines the distribution of synthetic values. 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 density-based transformations that preserve the distribution of the confidential data, up to sampling error, on specified subdomains. We demonstrate through simulations and a large scale application that our approach preserves important statistical properties of the confidential data, including higher moments, with low disclosure risk.

Date: 2009
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Citations: View citations in EconPapers (3)

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Related works:
Working Paper: Distribution-Preserving Statistical Disclosure Limitation (2007)
Working Paper: Distribution Preserving Statistical Disclosure Limitation (2006)
Working Paper: Distribution-Preserving Statistical Disclosure Limitation (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:53:y:2009:i:12:p:4228-4242

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