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Distribution Preserving Statistical Disclosure Limitation

Author

Listed:
  • Simon D. Woodcock
  • Gary Benedetto

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 because the distribution of synthetic data is completely determined by the model used to generate them. We present two practical methods of generating synthetic values when the imputer has only limited information about the true data generating process. One is applicable when the true likelihood is known up to a monotone transformation. The second requires only limited knowledge of the true likelihood, but nevertheless preserves the conditional distribution of the confidential data, up to sampling error, on arbitrary subdomains. Our method maximizes data utility and minimizes incremental disclosure risk up to posterior uncertainty in the imputation model and sampling error in the estimated transformation. We validate the approach with a simulation and application to a large linked employer-employee database.

Suggested Citation

  • Simon D. Woodcock & Gary Benedetto, 2006. "Distribution Preserving Statistical Disclosure Limitation," Longitudinal Employer-Household Dynamics Technical Papers 2006-04, Center for Economic Studies, U.S. Census Bureau.
  • Handle: RePEc:cen:tpaper:2006-04
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    File URL: https://www2.census.gov/ces/tp/tp-2006-04.pdf
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    References listed on IDEAS

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    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. 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.
    3. 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.
    4. 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.
    5. 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.
    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

    Keywords

    statistical disclosure limitation; con?dentiality; 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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