IDEAS home Printed from https://ideas.repec.org/a/bla/istatr/v88y2020i2p354-379.html
   My bibliography  Save this article

Re‐identification in the Absence of Common Variables for Matching

Author

Listed:
  • Duncan Smith

Abstract

A basic concern in statistical disclosure limitation is the re‐identification of individuals in anonymised microdata. Linking against a second dataset that contains identifying information can result in a breach of confidentiality. Almost all linkage approaches are based on comparing the values of variables that are common to both datasets. It is tempting to think that if datasets contain no common variables, then there can be no risk of re‐identification. However, linkage has been attempted between such datasets via the extraction of structural information using ordered weighted averaging (OWA) operators. Although this approach has been shown to perform better than randomly pairing records, it is debatable whether it demonstrates a practically significant disclosure risk. This paper reviews some of the main aspects of statistical disclosure limitation. It then goes on to show that a relatively simple, supervised Bayesian approach can consistently outperform OWA linkage. Furthermore, the Bayesian approach demonstrates a significant risk of re‐identification for the types of data considered in the OWA record linkage literature.

Suggested Citation

  • Duncan Smith, 2020. "Re‐identification in the Absence of Common Variables for Matching," International Statistical Review, International Statistical Institute, vol. 88(2), pages 354-379, August.
    • Handle: RePEc:bla:istatr:v:88:y:2020:i:2:p:354-379
    DOI: 10.1111/insr.12353
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/insr.12353
    Download Restriction: no
    File URL: https://libkey.io/10.1111/insr.12353?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. Mauricio Sadinle & Stephen E. Fienberg, 2013. "A Generalized Fellegi--Sunter Framework for Multiple Record Linkage With Application to Homicide Record Systems," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(502), pages 385-397, June.
    2. Roee Gutman & Christopher C. Afendulis & Alan M. Zaslavsky, 2013. "A Bayesian Procedure for File Linking to Analyze End-of-Life Medical Costs," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 34-47, March.
    3. 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.
    4. Rebecca C. Steorts & Rob Hall & Stephen E. Fienberg, 2016. "A Bayesian Approach to Graphical Record Linkage and Deduplication," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1660-1672, October.
    5. H. W. Kuhn, 1955. "The Hungarian method for the assignment problem," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 2(1‐2), pages 83-97, March.
    6. Paass, Gerhard, 1988. "Disclosure Risk and Disclosure Avoidance for Microdata," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(4), pages 487-500, October.
    7. Jonathan J. Forster & Emily L. Webb, 2007. "Bayesian disclosure risk assessment: predicting small frequencies in contingency tables," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 56(5), pages 551-570, November.
    8. Mauricio Sadinle, 2017. "Bayesian Estimation of Bipartite Matchings for Record Linkage," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 600-612, April.
    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. Betancourt, Brenda & Sosa, Juan & Rodríguez, Abel, 2022. "A prior for record linkage based on allelic partitions," Computational Statistics & Data Analysis, Elsevier, vol. 172(C).
    2. Natalie Shlomo & Chris Skinner, 2022. "Measuring risk of re‐identification in microdata: State‐of‐the art and new directions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1644-1662, October.
    3. Daniel H. Weinberg & John M. Abowd & Robert F. Belli & Noel Cressie & David C. Folch & Scott H. Holan & Margaret C. Levenstein & Kristen M. Olson & Jerome P. Reiter & Matthew D. Shapiro & Jolene Smyth, 2017. "Effects of a Government-Academic Partnership: Has the NSF-Census Bureau Research Network Helped Improve the U.S. Statistical System?," Working Papers 17-59r, Center for Economic Studies, U.S. Census Bureau.
    4. Shlomo, Natalie & Skinner, Chris, 2022. "Measuring risk of re-identification in microdata: state-of-the art and new directions," LSE Research Online Documents on Economics 117168, London School of Economics and Political Science, LSE Library.
    5. Bera Sabyasachi & Chatterjee Snigdhansu, 2020. "High dimensional, robust, unsupervised record linkage," Statistics in Transition New Series, Statistics Poland, vol. 21(4), pages 123-143, August.
    6. Sabyasachi Bera & Snigdhansu Chatterjee, 2020. "High dimensional, robust, unsupervised record linkage," Statistics in Transition New Series, Polish Statistical Association, vol. 21(4), pages 123-143, August.
    7. John M. Abowd & Joelle Abramowitz & Margaret C. Levenstein & Kristin McCue & Dhiren Patki & Trivellore Raghunathan & Ann M. Rodgers & Matthew D. Shapiro & Nada Wasi & Dawn Zinsser, 2021. "Finding Needles in Haystacks: Multiple-Imputation Record Linkage Using Machine Learning," Working Papers 21-35, Center for Economic Studies, U.S. Census Bureau.
    8. Skinner, Chris J., 2007. "The probability of identification: applying ideas from forensic statistics to disclosure risk assessment," LSE Research Online Documents on Economics 39105, London School of Economics and Political Science, LSE Library.
    9. Thomas Stringham, 2022. "Fast Bayesian Record Linkage With Record-Specific Disagreement Parameters," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(4), pages 1509-1522, October.
    10. Vo, Thanh Huan & Chauvet, Guillaume & Happe, André & Oger, Emmanuel & Paquelet, Stéphane & Garès, Valérie, 2023. "Extending the Fellegi-Sunter record linkage model for mixed-type data with application to the French national health data system," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
    11. C. J. Skinner, 2007. "The probability of identification: applying ideas from forensic statistics to disclosure risk assessment," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(1), pages 195-212, January.
    12. Li‐Chun Zhang & Tiziana Tuoto, 2021. "Linkage‐data linear regression," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(2), pages 522-547, April.
    13. András Frank, 2005. "On Kuhn's Hungarian Method—A tribute from Hungary," Naval Research Logistics (NRL), John Wiley & Sons, vol. 52(1), pages 2-5, February.
    14. Amit Kumar & Anila Gupta, 2013. "Mehar’s methods for fuzzy assignment problems with restrictions," Fuzzy Information and Engineering, Springer, vol. 5(1), pages 27-44, March.
    15. Parvin Ahmadi & Iman Gholampour & Mahmoud Tabandeh, 2018. "Cluster-based sparse topical coding for topic mining and document clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(3), pages 537-558, September.
    16. Chenchen Ma & Jing Ouyang & Gongjun Xu, 2023. "Learning Latent and Hierarchical Structures in Cognitive Diagnosis Models," Psychometrika, Springer;The Psychometric Society, vol. 88(1), pages 175-207, March.
    17. Aidin Rezaeian & Hamidreza Koosha & Mohammad Ranjbar & Saeed Poormoaied, 2024. "The assignment of project managers to projects in an uncertain dynamic environment," Annals of Operations Research, Springer, vol. 341(2), pages 1107-1134, October.
    18. Tran Hoang Hai, 2020. "Estimation of volatility causality in structural autoregressions with heteroskedasticity using independent component analysis," Statistical Papers, Springer, vol. 61(1), pages 1-16, February.
    19. Kristian Lum & Megan Emily Price & David Banks, 2013. "Rejoinder," The American Statistician, Taylor & Francis Journals, vol. 67(4), pages 205-206, November.
    20. Caplin, Andrew & Leahy, John, 2020. "Comparative statics in markets for indivisible goods," Journal of Mathematical Economics, Elsevier, vol. 90(C), pages 80-94.

    More about this item

    Statistics

    Access and download statistics

    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:bla:istatr:v:88:y:2020:i:2:p:354-379. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/isiiinl.html .

    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.