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Disclosure Limitation and Confidentiality Protection in Linked Data

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  • John M. Abowd
  • Ian M. Schmutte
  • Lars Vilhuber

Abstract

Confidentiality protection for linked administrative data is a combination of access modalities and statistical disclosure limitation. We review traditional statistical disclosure limitation methods and newer methods based on synthetic data, input noise infusion and formal privacy. We discuss how these methods are integrated with access modalities by providing three detailed examples. The first example is the linkages in the Health and Retirement Study to Social Security Administration data. The second example is the linkage of the Survey of Income and Program Participation to administrative data from the Internal Revenue Service and the Social Security Administration. The third example is the Longitudinal Employer-Household Dynamics data, which links state unemployment insurance records for workers and firms to a wide variety of censuses and surveys at the U.S. Census Bureau. For examples, we discuss access modalities, disclosure limitation methods, the effectiveness of those methods, and the resulting analytical validity. The final sections discuss recent advances in access modalities for linked administrative data.

Suggested Citation

  • John M. Abowd & Ian M. Schmutte & Lars Vilhuber, 2018. "Disclosure Limitation and Confidentiality Protection in Linked Data," Working Papers 18-07, Center for Economic Studies, U.S. Census Bureau.
  • Handle: RePEc:cen:wpaper:18-07
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    File URL: https://www2.census.gov/ces/wp/2018/CES-WP-18-07.pdf
    File Function: First version, 2018
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    References listed on IDEAS

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    1. Bethany DeSalvo & Frank F. Limehouse & Shawn D. Klimek, 2016. "Documenting the Business Register and Related Economic Business Data," Working Papers 16-17, Center for Economic Studies, U.S. Census Bureau.
    2. Holan, Scott H. & Toth, Daniell & Ferreira, Marco A. R. & Karr, Alan F., 2010. "Bayesian Multiscale Multiple Imputation With Implications for Data Confidentiality," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 564-577.
    3. Satkartar K. Kinney & Jerome P. Reiter & Arnold P. Reznek & Javier Miranda & Ron S. Jarmin & John M. Abowd, 2011. "Towards Unrestricted Public Use Business Microdata: The Synthetic Longitudinal Business Database," International Statistical Review, International Statistical Institute, vol. 79(3), pages 362-384, December.
    4. Giuseppe Bruno & Leandro D'Aurizio & Raffaele Tartaglia Polcini, 2008. "Remote processing of firm microdata at the Bank of Italy," Questioni di Economia e Finanza (Occasional Papers) 36, Bank of Italy, Economic Research and International Relations Area.
    5. John M. Abowd & Kaj Gittings & Kevin L. McKinney & Bryce E. Stephens & Lars Vilhuber & Simon Woodcock, 2012. "Dynamically Consistent Noise Infusion and Partially Synthetic Data as Confidentiality Protection Measures for Related Time Series," Working Papers 12-13, Center for Economic Studies, U.S. Census Bureau.
    6. John M. Abowd & Ian M. Schmutte, 2015. "Economic Analysis and Statistical Disclosure Limitation," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 46(1 (Spring), pages 221-293.
    7. 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.
    8. John M. Abowd & Ian M. Schmutte & Lars Vilhuber, 2018. "Disclosure Limitation and Confidentiality Protection in Linked Data," Working Papers 18-07, Center for Economic Studies, U.S. Census Bureau.
    9. John M. Abowd & Lars Vilhuber, 2012. "Did the Housing Price Bubble Clobber Local Labor Market Job and Worker Flows When It Burst?," American Economic Review, American Economic Association, vol. 102(3), pages 589-593, May.
    10. Ron S Jarmin & Javier Miranda, 2002. "The Longitudinal Business Database," Working Papers 02-17, Center for Economic Studies, U.S. Census Bureau.
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    1. John M. Abowd & Ian M. Schmutte & Lars Vilhuber, 2018. "Disclosure Limitation and Confidentiality Protection in Linked Data," Working Papers 18-07, Center for Economic Studies, U.S. Census Bureau.

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