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The probability of identification: applying ideas from forensic statistics to disclosure risk assessment

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  • Skinner, Chris J.

Abstract

The paper establishes a correspondence between statistical disclosure control and forensic statistics regarding their common use of the concept of ‘probability of identification’. The paper then seeks to investigate what lessons for disclosure control can be learnt from the forensic identification literature. The main lesson that is considered is that disclosure risk assessment cannot, in general, ignore the search method that is employed by an intruder seeking to achieve disclosure. The effects of using several search methods are considered. Through consideration of the plausibility of assumptions and ‘worst case’ approaches, the paper suggests how the impact of search method can be handled. The paper focuses on foundations of disclosure risk assessment, providing some justification for some modelling assumptions underlying some existing record level measures of disclosure risk. The paper illustrates the effects of using various search methods in a numerical example based on microdata from a sample from the 2001 UK census.

Suggested Citation

  • 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.
    • Handle: RePEc:ehl:lserod:39105
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    File URL: http://eprints.lse.ac.uk/39105/
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    References listed on IDEAS

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    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. Reiter, Jerome P., 2005. "Estimating Risks of Identification Disclosure in Microdata," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1103-1112, December.
    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. J. B. Copas & F. J. Hilton, 1990. "Record Linkage: Statistical Models for Matching Computer Records," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 153(3), pages 287-312, May.
    5. Angela Dale & Mark Elliot, 2001. "Proposals for 2001 samples of anonymized records: An assessment of disclosure risk," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(3), pages 427-447.
    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. David J. Balding & Peter Donnelly, 1995. "Inference in Forensic Identification," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 158(1), pages 21-40, January.
    8. 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.
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    Cited by:

    1. Sergio I. Prada & Claudia González-Martínez & Joshua Borton & Johannes Fernandes-Huessy & Craig Holden & Elizabeth Hair & and Tim Mulcahy, 2011. "Avoiding Disclosure of Individually Identifiable Health Information," SAGE Open, , vol. 1(3), pages 21582440114, October.
    2. Prada, Sergio I & Gonzalez, Claudia & Borton, Joshua & Fernandes-Huessy, Johannes & Holden, Craig & Hair, Elizabeth & Mulcahy, Tim, 2011. "Avoiding disclosure of individually identifiable health information: a literature review," MPRA Paper 35463, University Library of Munich, Germany.

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    More about this item

    Keywords

    confidentiality; disclosure control; microdata; record linkage; uniqueness;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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