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Identification, data combination and the risk of disclosure

Author

Listed:
  • Tatiana V. Komarova

    () (Institute for Fiscal Studies and London School of Economics and Political Science)

  • Denis Nekipelov

    (Institute for Fiscal Studies and Berkeley)

  • Evgeny Yakovlev

    (Institute for Fiscal Studies)

Abstract

Businesses routinely rely on econometric models to analyze and predict consumer behavior. Estimation of such models may require combining a firm's internal data with external datasets to take into account sample selection, missing observations, omitted variables and errors in measurement within the existing data source. In this paper we point out that these data problems can be addressed when estimating econometric models from combined data using the data mining techniques under mild assumptions regarding the data distribution. However, data combination leads to serious threats to security of consumer data: we demonstrate that point identification of an econometric model from combined data is incompatible with restrictions on the risk of individual disclosure. Consequently, if a consumer model is point identified, the firm would (implicitly or explicitly) reveal the identity of at least some of consumers in its internal data. More importantly, we provide an argument that unless the firm places a restriction on the individual disclosure risk when combining data, even if the raw combined dataset is not shared with a third party, an adversary or a competitor can gather confidential information regarding some individuals from the estimated model.

Suggested Citation

  • Tatiana V. Komarova & Denis Nekipelov & Evgeny Yakovlev, 2011. "Identification, data combination and the risk of disclosure," CeMMAP working papers CWP38/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  • Handle: RePEc:ifs:cemmap:38/11
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    File URL: http://cemmap.ifs.org.uk/wps/cwp3811.pdf
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    References listed on IDEAS

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    1. Avi Goldfarb & Catherine Tucker, 2011. "Online Display Advertising: Targeting and Obtrusiveness," Marketing Science, INFORMS, vol. 30(3), pages 389-404, 05-06.
    2. Calzolari, Giacomo & Pavan, Alessandro, 2006. "On the optimality of privacy in sequential contracting," Journal of Economic Theory, Elsevier, vol. 130(1), pages 168-204, September.
    3. Alessandro Acquisti & Hal R. Varian, 2005. "Conditioning Prices on Purchase History," Marketing Science, INFORMS, vol. 24(3), pages 367-381, May.
    4. Amalia R. Miller & Catherine Tucker, 2009. "Privacy Protection and Technology Diffusion: The Case of Electronic Medical Records," Management Science, INFORMS, vol. 55(7), pages 1077-1093, July.
    5. Thierry Magnac & Eric Maurin, 2008. "Partial Identification in Monotone Binary Models: Discrete Regressors and Interval Data," Review of Economic Studies, Oxford University Press, vol. 75(3), pages 835-864.
    6. Horowitz, Joel L. & Manski, Charles F., 2006. "Identification and estimation of statistical functionals using incomplete data," Journal of Econometrics, Elsevier, vol. 132(2), pages 445-459, June.
    7. Curtis R. Taylor, 2004. "Consumer Privacy and the Market for Customer Information," RAND Journal of Economics, The RAND Corporation, vol. 35(4), pages 631-650, Winter.
    8. Molinari, Francesca, 2008. "Partial identification of probability distributions with misclassified data," Journal of Econometrics, Elsevier, vol. 144(1), pages 81-117, May.
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    Cited by:

    1. David Pacini, 2012. "Least Square Linear Prediction with Two-Sample Data," Bristol Economics Discussion Papers 12/631, Department of Economics, University of Bristol, UK.

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