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The Risk of Disclosure for Microdata

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  • Duncan, George
  • Lambert, Diane

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  • 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.
    • Handle: RePEc:bes:jnlbes:v:7:y:1989:i:2:p:207-17
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    Citations

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    Cited by:

    1. S F Roehrig & R Padman & R Krishnan & G T Duncan, 2011. "Exact and heuristic methods for cell suppression in multi-dimensional linked tables," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(2), pages 291-304, February.
    2. Xiao-Bai Li & Jialun Qin, 2017. "Anonymizing and Sharing Medical Text Records," Information Systems Research, INFORMS, vol. 28(2), pages 332-352, June.
    3. 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.
    4. 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.
    5. 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.
    6. Xiao-Bai Li & Sumit Sarkar, 2009. "Against Classification Attacks: A Decision Tree Pruning Approach to Privacy Protection in Data Mining," Operations Research, INFORMS, vol. 57(6), pages 1496-1509, December.
    7. 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.
    8. 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.
    9. Walter Mãœller & Uwe Blien & Heike Wirth, 1995. "Identification Risks of Microdata," Sociological Methods & Research, , vol. 24(2), pages 131-157, November.
    10. 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.
    11. Kokolakis, G. & Fouskakis, D., 2009. "Importance partitioning in micro-aggregation," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2439-2445, May.
    12. 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.
    13. Xiao-Bai Li & Sumit Sarkar, 2013. "Class-Restricted Clustering and Microperturbation for Data Privacy," Management Science, INFORMS, vol. 59(4), pages 796-812, April.
    14. 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.
    15. Gilboa-Freedman, Gail & Smorodinsky, Rann, 2020. "On the properties that characterize privacy," Mathematical Social Sciences, Elsevier, vol. 103(C), pages 59-68.
    16. 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.
    17. 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.
    18. Loong Bronwyn & Rubin Donald B., 2017. "Multiply-Imputed Synthetic Data: Advice to the Imputer," Journal of Official Statistics, Sciendo, vol. 33(4), pages 1005-1019, December.
    19. Xiao-Bai Li & Sumit Sarkar, 2006. "Privacy Protection in Data Mining: A Perturbation Approach for Categorical Data," Information Systems Research, INFORMS, vol. 17(3), pages 254-270, September.
    20. Myron Gutmann & Kristine Witkowski & Corey Colyer & JoAnne O’Rourke & James McNally, 2008. "Providing Spatial Data for Secondary Analysis: Issues and Current Practices Relating to Confidentiality," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 27(6), pages 639-665, December.
    21. 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.
    22. 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.
    23. 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.
    24. 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.
    25. 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.

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