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- W1493262437 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." @default.
- W1493262437 created "2016-06-24" @default.
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- W1493262437 date "2006-09-01" @default.
- W1493262437 modified "2023-09-24" @default.
- W1493262437 title "Distribution-Preserving Statistical Disclosure Limitation" @default.
- W1493262437 hasPublicationYear "2006" @default.
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