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Releasing multiply-imputed synthetic data generated in two stages to protect confidentiality

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  • Reiter, Jerome P.
  • Drechsler, Jörg

Abstract

"To protect the cofidentiality of survey respondents' identities and sensitive attributes, statistical agencies can release data in which cofidential values are replaced with multiple imputations. These are called synthetic data. We propose a two-stage approach to generating synthetic data that enables agencies to release different numbers of imputations for different variables. Generation in two stages can reduce computational burdens, decrease disclosure risk, and increase inferential accuracy relative to generation in one stage. We present methods for obtaining inferences from such data. We describe the application of two stage synthesis to creating a public use file for a German business database." (Author's abstract, IAB-Doku) ((en))

Suggested Citation

  • Reiter, Jerome P. & Drechsler, Jörg, 2007. "Releasing multiply-imputed synthetic data generated in two stages to protect confidentiality," IAB-Discussion Paper 200720, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    • Handle: RePEc:iab:iabdpa:200720
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    References listed on IDEAS

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    1. Donald B. Rubin, 2003. "Nested multiple imputation of NMES via partially incompatible MCMC," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 57(1), pages 3-18, February.
    2. C. J. Skinner & M. J. Elliot, 2002. "A measure of disclosure risk for microdata," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 855-867, October.
    3. Drechsler, Jörg & Dundler, Agnes & Bender, Stefan & Rässler, Susanne & Zwick, Thomas, 2007. "A new approach for disclosure control in the IAB Establishment Panel : multiple imputation for a better data access," IAB-Discussion Paper 200711, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    4. John M. Abowd & Julia I. Lane, 2004. "New Approaches to Confidentiality Protection Synthetic Data, Remote Access and Research Data Centers," Longitudinal Employer-Household Dynamics Technical Papers 2004-03, Center for Economic Studies, U.S. Census Bureau.
    5. Karr, A.F. & Kohnen, C.N. & Oganian, A. & Reiter, J.P. & Sanil, A.P., 2006. "A Framework for Evaluating the Utility of Data Altered to Protect Confidentiality," The American Statistician, American Statistical Association, vol. 60, pages 224-232, August.
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    Cited by:

    1. Jörg Höhne, 2008. "Anonymisierungsverfahren für Paneldaten," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 2(3), pages 259-275, October.
    2. Humera Razzak & Christian Heumann, 2019. "Hybrid Multiple Imputation In A Large Scale Complex Survey," Statistics in Transition New Series, Polish Statistical Association, vol. 20(4), pages 33-58, December.
    3. Razzak Humera & Heumann Christian, 2019. "Hybrid Multiple Imputation In A Large Scale Complex Survey," Statistics in Transition New Series, Polish Statistical Association, vol. 20(4), pages 33-58, December.
    4. Jan Pablo Burgard & Jan-Philipp Kolb & Hariolf Merkle & Ralf Münnich, 2017. "Synthetic data for open and reproducible methodological research in social sciences and official statistics," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 11(3), pages 233-244, December.

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