IDEAS home Printed from https://ideas.repec.org/a/sae/somere/v24y1995i2p131-157.html
   My bibliography  Save this article

Identification Risks of Microdata

Author

Listed:
  • WALTER MÃœLLER

    (Universität Mannheim, Germany)

  • UWE BLIEN

    (Bundesanstalt für Arbeit, Germany)

  • HEIKE WIRTH

    (Zentrum für Umfragen, Methoden und Analysen, Germany)

Abstract

In the social sciences and in other fields where data from individual respondents are collected, it is an important concern that information remains confidential and that the identity of individual data providers is not disclosed. For several realistic scenarios, the present article examines the risks of discovering the identity of data providers even in cases where the microdata have been previously anonymized by some procedure. The results show that, even under peculiarly risky conditions, the identification risk is smaller than has been assumed by previous research. The inherent unreliability of measurement is found to be an implicit protection of anonymity and a natural barrier to identification.

Suggested Citation

  • Walter Mãœller & Uwe Blien & Heike Wirth, 1995. "Identification Risks of Microdata," Sociological Methods & Research, , vol. 24(2), pages 131-157, November.
    • Handle: RePEc:sae:somere:v:24:y:1995:i:2:p:131-157
    DOI: 10.1177/0049124195024002001
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0049124195024002001
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0049124195024002001?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. Catherine Marsh & Chris Skinner & Sara Arber & Bruce Penhale & Stan Openshaw & John Hobcraft & Denise Lievesley & Nigel Walford, 1991. "The Case for Samples of Anonymized Records from the 1991 Census," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 154(2), pages 305-340, March.
    2. B.V. Greenberg & L.V. Zayatz, 1992. "Strategies for measuring risk in public use microdata files," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 46(1), pages 33-48, March.
    3. C.J. Skinner, 1992. "On identification disclosure and prediction disclosure for microdata," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 46(1), pages 21-32, March.
    4. Duncan, George & Lambert, Diane, 1989. "The Risk of Disclosure for Microdata," Journal of Business & Economic Statistics, American Statistical Association, vol. 7(2), pages 207-217, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bender, Stefan & Hilzendegen, Jürgen, 1995. "Die IAB-Beschäftigtenstrichprobe als scientific use file," Mitteilungen aus der Arbeitsmarkt- und Berufsforschung, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 28(1), pages 76-95.
    2. Elliot Mark & Mackey Elaine & O’Shea Susan & Tudor Caroline & Spicer Keith, 2016. "End User Licence to Open Government Data? A Simulated Penetration Attack on Two Social Survey Datasets," Journal of Official Statistics, Sciendo, vol. 32(2), pages 329-348, June.
    3. Bender, Stefan & Hilzendegen, Jürgen, 1995. "Die IAB-Beschäftigtenstrichprobe als scientific use file," Mitteilungen aus der Arbeitsmarkt- und Berufsforschung, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 28(1), pages 76-95.
    4. Bender, Stefan & Hilzendegen, Jürgen, 1995. "Die IAB-Beschäftigtenstrichprobe als scientific use file," Mitteilungen aus der Arbeitsmarkt- und Berufsforschung, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 28(1), pages 76-95.

    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. 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.
    2. James Jackson & Robin Mitra & Brian Francis & Iain Dove, 2022. "Using saturated count models for user‐friendly synthesis of large confidential administrative databases," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1613-1643, October.
    3. 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.
    4. 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.
    5. Bender, Stefan & Hilzendegen, Jürgen, 1995. "Die IAB-Beschäftigtenstrichprobe als scientific use file," Mitteilungen aus der Arbeitsmarkt- und Berufsforschung, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 28(1), pages 76-95.
    6. Bender, Stefan & Hilzendegen, Jürgen, 1995. "Die IAB-Beschäftigtenstrichprobe als scientific use file," Mitteilungen aus der Arbeitsmarkt- und Berufsforschung, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 28(1), pages 76-95.
    7. 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.
    8. Xiao-Bai Li & Jialun Qin, 2017. "Anonymizing and Sharing Medical Text Records," Information Systems Research, INFORMS, vol. 28(2), pages 332-352, June.
    9. Shlomo, Natalie & Skinner, Chris J., 2010. "Assessing the protection provided by misclassification-based disclosure limitation methods for survey microdata," LSE Research Online Documents on Economics 39119, London School of Economics and Political Science, LSE Library.
    10. Skinner, Chris J. & Shlomo, Natalie, 2008. "Assessing identification risk in survey microdata using log-linear models," LSE Research Online Documents on Economics 39112, London School of Economics and Political Science, LSE Library.
    11. Sumit Dutta Chowdhury & George T. Duncan & Ramayya Krishnan & Stephen F. Roehrig & Sumitra Mukherjee, 1999. "Disclosure Detection in Multivariate Categorical Databases: Auditing Confidentiality Protection Through Two New Matrix Operators," Management Science, INFORMS, vol. 45(12), pages 1710-1723, December.
    12. D Martin & M L Senior & H C W L Williams, 1994. "On Measures of Deprivation and the Spatial Allocation of Resources for Primary Health Care," Environment and Planning A, , vol. 26(12), pages 1911-1929, December.
    13. Braathen, Christian & Thorsen, Inge & Ubøe, Jan, 2022. "Adjusting for Cell Suppression in Commuting Trip Data," Discussion Papers 2022/13, Norwegian School of Economics, Department of Business and Management Science.
    14. Bender, Stefan & Hilzendegen, Jürgen, 1995. "Die IAB-Beschäftigtenstrichprobe als scientific use file," Mitteilungen aus der Arbeitsmarkt- und Berufsforschung, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 28(1), pages 76-95.
    15. Gilboa-Freedman, Gail & Smorodinsky, Rann, 2020. "On the properties that characterize privacy," Mathematical Social Sciences, Elsevier, vol. 103(C), pages 59-68.
    16. Shaobo Li & Matthew J. Schneider & Yan Yu & Sachin Gupta, 2023. "Reidentification Risk in Panel Data: Protecting for k -Anonymity," Information Systems Research, INFORMS, vol. 34(3), pages 1066-1088, September.
    17. George Kokolakis & Dimitris Fouskakis, 2008. "On the Discrepancy Measures for the Optimal Equal Probability Partitioning in Bayesian Multivariate Micro-Aggregation," Journal of Classification, Springer;The Classification Society, vol. 25(2), pages 209-224, November.
    18. Kokolakis, G. & Fouskakis, D., 2009. "Importance partitioning in micro-aggregation," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2439-2445, May.
    19. Christine M. O'Keefe & James O. Chipperfield, 2013. "A Summary of Attack Methods and Confidentiality Protection Measures for Fully Automated Remote Analysis Systems," International Statistical Review, International Statistical Institute, vol. 81(3), pages 426-455, December.
    20. Xiao-Bai Li & Sumit Sarkar, 2013. "Class-Restricted Clustering and Microperturbation for Data Privacy," Management Science, INFORMS, vol. 59(4), pages 796-812, April.

    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:sae:somere:v:24:y:1995:i:2:p:131-157. 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: SAGE Publications (email available below). General contact details of provider: .

    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.