IDEAS home Printed from https://ideas.repec.org/a/vrs/offsta/v31y2015i1p121-138n6.html
   My bibliography  Save this article

Statistical Disclosure Limitation in the Presence of Edit Rules

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/jos-2015-0006
    Download Restriction: no

    File URL: https://libkey.io/10.1515/jos-2015-0006?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hang J. Kim & Jörg Drechsler & Katherine J. Thompson, 2021. "Synthetic microdata for establishment surveys under informative sampling," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 255-281, January.
    2. Wu, Fang & Swait, Joffre & Chen, Yuxin, 2019. "Feature-based attributes and the roles of consumers' perception bias and inference in choice," International Journal of Research in Marketing, Elsevier, vol. 36(2), pages 325-340.
    3. Weining Li & Meilin Zhang & Heng Du & Jianliang Wu & Lei Zhou & Jianfeng Liu, 2024. "Multi-Trait Bayesian Models Enhance the Accuracy of Genomic Prediction in Multi-Breed Reference Populations," Agriculture, MDPI, vol. 14(4), pages 1-19, April.
    4. Akinc, Deniz & Vandebroek, Martina, 2018. "Bayesian estimation of mixed logit models: Selecting an appropriate prior for the covariance matrix," Journal of choice modelling, Elsevier, vol. 29(C), pages 133-151.
    5. Goldstein Harvey & Shlomo Natalie, 2020. "A Probabilistic Procedure for Anonymisation, for Assessing the Risk of Re-identification and for the Analysis of Perturbed Data Sets," Journal of Official Statistics, Sciendo, vol. 36(1), pages 89-115, March.
    6. Ian Lundberg & Arvind Narayanan & Karen Levy & Matthew Salganik, 2018. "Privacy, ethics, and data access: A case study of the Fragile Families Challenge," Working Papers wp18-09-ff, Princeton University, School of Public and International Affairs, Center for Research on Child Wellbeing..
    7. Chenyang Gu & Haiden Huskamp & Julie Donohue & Sharon‐Lise Normand, 2021. "A Bayesian hierarchical model for characterizing the diffusion of new antipsychotic drugs," Biometrics, The International Biometric Society, vol. 77(2), pages 649-660, June.
    8. Junhao Pan & Edward Haksing Ip & Laurette Dubé, 2020. "Multilevel Heterogeneous Factor Analysis and Application to Ecological Momentary Assessment," Psychometrika, Springer;The Psychometric Society, vol. 85(1), pages 75-100, March.
    9. Tapan K. Nayak & Samson A. Adeshiyan, 2016. "On Invariant Post-randomization for Statistical Disclosure Control," International Statistical Review, International Statistical Institute, vol. 84(1), pages 26-42, April.
    10. Harm Jan Boonstra & Jan van den Brakel & Sumonkanti Das, 2021. "Multilevel time series modelling of mobility trends in the Netherlands for small domains," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(3), pages 985-1007, July.
    11. Chipperfield James O., 2014. "Disclosure-Protected Inference with Linked Microdata Using a Remote Analysis Server," Journal of Official Statistics, Sciendo, vol. 30(1), pages 123-146, March.
    12. Hang J. Kim & Jerome P. Reiter & Alan F. Karr, 2018. "Simultaneous edit-imputation and disclosure limitation for business establishment data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(1), pages 63-82, January.
    13. Karr Alan F., 2013. "Discussion," Journal of Official Statistics, Sciendo, vol. 29(1), pages 157-163, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:vrs:offsta:v:31:y:2015:i:1:p:121-138:n:6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.