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Using Multiple Imputation to Integrate and Disseminate Confidential Microdata

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  • Jerome P. Reiter

Abstract

In data integration contexts, two statistical agencies seek to merge their separate databases into one file. The agencies also may seek to disseminate data to the public based on the integrated file. These goals may be complicated by the agencies' need to protect the confidentiality of database subjects, which could be at risk during the integration or dissemination stage. This article proposes several approaches based on multiple imputation for disclosure limitation, usually called synthetic data, that could be used to facilitate data integration and dissemination while protecting data confidentiality. It reviews existing methods for obtaining inferences from synthetic data and points out where new methods are needed to implement the data integration proposals. Dans les contextes d'intégration de données, deux agences statistiques cherchent à fusionner leurs bases de données séparées en un fichier. Les agences peuvent aussi chercher à diffuser au public les données issues du fichier intégré. Ces objectifs peuvent être compliqués par le besoin de protéger la confidentialité des objets de la base de données, qui pourrait être menacé pendant la phase d'intégration et de diffusion. Cet article propose plusieurs approches basées sur l'imputation multiple pour limiter la divulgation, qu'on appelle habituellement données synthétiques, qui pourraient être utilisées pour faciliter l'intégration et la diffusion des données tout en protégeant leur confidentialité. Il passe en revue les méthodes existantes pour obtenir des inférences à partir de données synthétiques et montre les cas où l'on a besoin de nouvelles méthodes pour mettre en œuvre les propositions d'intégration de données.

Suggested Citation

  • Jerome P. Reiter, 2009. "Using Multiple Imputation to Integrate and Disseminate Confidential Microdata," International Statistical Review, International Statistical Institute, vol. 77(2), pages 179-195, August.
    • Handle: RePEc:bla:istatr:v:77:y:2009:i:2:p:179-195
    DOI: 10.1111/j.1751-5823.2009.00083.x
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    References listed on IDEAS

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    1. Christine N. Kohnen & Jerome P. Reiter, 2009. "Multiple imputation for combining confidential data owned by two agencies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(2), pages 511-528, April.
    2. 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].
    3. Ghosh, Joyee & Reiter, Jerome P. & Karr, Alan F., 2007. "Secure computation with horizontally partitioned data using adaptive regression splines," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5813-5820, August.
    4. Natalie Shlomo, 2007. "Statistical Disclosure Control Methods for Census Frequency Tables," International Statistical Review, International Statistical Institute, vol. 75(2), pages 199-217, August.
    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. Jerome P. Reiter, 2005. "Releasing multiply imputed, synthetic public use microdata: an illustration and empirical study," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(1), pages 185-205, January.
    7. Reiter, Jerome P. & Oganian, Anna & Karr, Alan F., 2009. "Verification servers: Enabling analysts to assess the quality of inferences from public use data," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1475-1482, February.
    8. Reiter, Jerome P., 2008. "Selecting the number of imputed datasets when using multiple imputation for missing data and disclosure limitation," Statistics & Probability Letters, Elsevier, vol. 78(1), pages 15-20, January.
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    Cited by:

    1. Claire McKay Bowen & Fang Liu & Bingyue Su, 2021. "Differentially private data release via statistical election to partition sequentially," METRON, Springer;Sapienza Università di Roma, vol. 79(1), pages 1-31, April.
    2. Andreas Alfons & Stefan Kraft & Matthias Templ & Peter Filzmoser, 2011. "Simulation of close-to-reality population data for household surveys with application to EU-SILC," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(3), pages 383-407, August.

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