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Distribution-Preserving Statistical Disclosure Limitation




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.

Suggested Citation

  • Simon D. Woodcock & Gary Benedetto, 2007. "Distribution-Preserving Statistical Disclosure Limitation," Discussion Papers dp07-15, Department of Economics, Simon Fraser University.
  • Handle: RePEc:sfu:sfudps:dp07-15

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    References listed on IDEAS

    1. Rathindra Sarathy & Krishnamurty Muralidhar & Rahul Parsa, 2002. "Perturbing Nonnormal Confidential Attributes: The Copula Approach," Management Science, INFORMS, vol. 48(12), pages 1613-1627, December.
    2. Reiter, Jerome P., 2005. "Estimating Risks of Identification Disclosure in Microdata," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1103-1112, December.
    3. John J. Abowd & John Haltiwanger & Julia Lane, 2004. "Integrated Longitudinal Employer-Employee Data for the United States," American Economic Review, American Economic Association, vol. 94(2), pages 224-229, May.
    4. John M. Abowd & Bryce E. Stephens & Lars Vilhuber & Fredrik Andersson & Kevin L. McKinney & Marc Roemer & Simon Woodcock, 2009. "The LEHD Infrastructure Files and the Creation of the Quarterly Workforce Indicators," NBER Chapters,in: Producer Dynamics: New Evidence from Micro Data, pages 149-230 National Bureau of Economic Research, Inc.
    5. Reiter, Jerome P. & Raghunathan, Trivellore E., 2007. "The Multiple Adaptations of Multiple Imputation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1462-1471, December.
    6. John M. Abowd & Paul A. Lengermann & Kevin L. McKinney, 2002. "The Measurement of Human Capital in the U.S. Economy," Longitudinal Employer-Household Dynamics Technical Papers 2002-09, Center for Economic Studies, U.S. Census Bureau, revised Mar 2003.
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    Cited by:

    1. Drechsler, Jörg & Reiter, Jerome P., 2011. "An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3232-3243, December.

    More about this item


    statistical disclosure limitation; confidentiality; privacy; multiple imputation; partially synthetic data;

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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