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Statistical Disclosure Limitation in the Presence of Edit Rules

Author

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  • Kim Hang J.

    (Duke University and National Institute of Statistical Sciences, P.O. Box 90251, Durham, NC 27708, U.S.A)

  • Karr Alan F.

    (RTI International, 3040 East Cornwallis Road, P.O. Box 12194, Research Triangle Park, NC 27709, U.S.A)

  • Reiter Jerome P.

    (Department of Statistical Science, Duke University, P.O. Box 90251, Durham, NC 27708, U.S.A)

Abstract

We compare two general strategies for performing statistical disclosure limitation (SDL) for continuous microdata subject to edit rules. In the first, existing SDL methods are applied, and any constraint-violating values they produce are replaced using a constraint-preserving imputation procedure. In the second, the SDL methods are modified to prevent them from generating violations. We present a simulation study, based on data from the Colombian Annual Manufacturing Survey, that evaluates the performance of the two strategies as applied to several SDL methods. The results suggest that differences in risk-utility profiles across SDL methods dwarf differences between the two general strategies. Among the SDL strategies, variants of microaggregation and partially synthetic data offer the most attractive risk-utility profiles.

Suggested Citation

  • Kim Hang J. & Karr Alan F. & Reiter Jerome P., 2015. "Statistical Disclosure Limitation in the Presence of Edit Rules," Journal of Official Statistics, Sciendo, vol. 31(1), pages 121-138, March.
    • Handle: RePEc:vrs:offsta:v:31:y:2015:i:1:p:121-138:n:6
    DOI: 10.1515/jos-2015-0006
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    References listed on IDEAS

    as
    1. Lawrence H. Cox & Alan F. Karr & Satkartar K. Kinney, 2011. "Risk‐Utility Paradigms for Statistical Disclosure Limitation: How to Think, But Not How to Act," International Statistical Review, International Statistical Institute, vol. 79(2), pages 160-183, August.
    2. O’Malley, A. James & Zaslavsky, Alan M., 2008. "Domain-Level Covariance Analysis for Multilevel Survey Data With Structured Nonresponse," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1405-1418.
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