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K-anonymity: A note on the trade-off between data utility and data security

Author

Listed:
  • Komarova, Tatiana

    () (London School of Economics and Political Science, London, UK)

  • Nekipelov, Denis

    () (University of Virginia, Charlottesville, US)

  • Al Rafi , Ahnaf

    () (London School of Economics and Political Science, London, UK)

  • Yakovlev, Evgeny

    () (New Economic School, Moscow, Russia)

Abstract

Researchers often use data from multiple datasets to conduct credible econometric and statistical analysis. The most reliable way to link entries across such datasets is to exploit unique identifiers if those are available. Such linkage however may result in privacy violations revealing sensitive information about some individuals in a sample. Thus, a data curator with concerns for individual privacy may choose to remove certain individual information from the private dataset they plan on releasing to researchers. The extent of individual information the data curator keeps in the private dataset can still allow a researcher to link the datasets, most likely with some errors, and usually results in a researcher having several feasible combined datasets. One conceptual framework a data curator may rely on is k-anonymity, k>=2, which gained wide popularity in computer science and statistical community. To ensure k-anonymity, the data curator releases only the amount of identifying information in the private dataset that guarantees that every entry in it can be linked to at least k different entries in the publicly available datasets the researcher will use. In this paper, we look at the data combination task and the estimation task from both perspectives – from the perspective of the researcher estimating the model and from the perspective of a data curator who restricts identifying information in the private dataset to make sure that k-anonymity holds. We illustrate how to construct identifiers in practice and use them to combine some entries across two datasets. We also provide an empirical illustration on how a data curator can ensure k-anonymity and consequences it has on the estimation procedure. Naturally, the utility of the combined data gets smaller as k increases, which is also evident from our empirical illustration.

Suggested Citation

  • Komarova, Tatiana & Nekipelov, Denis & Al Rafi , Ahnaf & Yakovlev, Evgeny, 2017. "K-anonymity: A note on the trade-off between data utility and data security," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 48, pages 44-62.
  • Handle: RePEc:ris:apltrx:0330
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    References listed on IDEAS

    as
    1. Tatiana Komarova & Denis Nekipelov & Evgeny Yakovlev, 2015. "Estimation of Treatment Effects from Combined Data: Identification versus Data Security," NBER Chapters,in: Economic Analysis of the Digital Economy, pages 279-308 National Bureau of Economic Research, Inc.
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    More about this item

    Keywords

    data protection; data combination; k-anonymity;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

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