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Robustification of Microdata Masking Methods and the Comparison with Existing Methods

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Privacy in Statistical Databases (PSD 2008)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5262))

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Abstract

The aim of this study was to compare different microdata protection methods for numerical variables under various conditions. Most of the methods used in this paper have been implemented in the R-package sdcMicro which is available for free on the comprehensive R archive network ( http://cran.r-project.org ). The other methods used can be easily applied using other R-packages. While most methods work well for homogeneous data sets, some methods fail completely when confidential variables contain outliers which is almost always the case with data from official statistics. To overcome these problems we have robustified popular methods such as microaggregation or shuffling which is based on a regression model. All methods have beed tested on bivariate data sets featuring different outlier scenarios. Additionally, a simulation study was performed.

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Authors and Affiliations

  1. Department of Methodology, Statistics Austria, Guglgasse 13, 1110, Vienna, Austria

    Matthias Templ & Bernhard Meindl

  2. Department of Statistics and Probability Theory, Vienna University of Technology, Wiedner Hauptstr, 8-10, 1040, Vienna, Austria

    Matthias Templ

Authors
  1. Matthias Templ
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  2. Bernhard Meindl
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Josep Domingo-Ferrer Yücel Saygın

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Templ, M., Meindl, B. (2008). Robustification of Microdata Masking Methods and the Comparison with Existing Methods. In: Domingo-Ferrer, J., Saygın, Y. (eds) Privacy in Statistical Databases. PSD 2008. Lecture Notes in Computer Science, vol 5262. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87471-3_10

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  • DOI: https://doi.org/10.1007/978-3-540-87471-3_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87470-6

  • Online ISBN: 978-3-540-87471-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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