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Statistical Disclosure Control for Micro-Data Using the R Package sdcMicro

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  • Templ, Matthias
  • Kowarik, Alexander
  • Meindl, Bernhard

Abstract

The demand for data from surveys, censuses or registers containing sensible information on people or enterprises has increased significantly over the last years. However, before data can be provided to the public or to researchers, confidentiality has to be respected for any data set possibly containing sensible information about individual units. Confidentiality can be achieved by applying statistical disclosure control (SDC) methods to the data in order to decrease the disclosure risk of data.The R package sdcMicro serves as an easy-to-handle, object-oriented S4 class implementation of SDC methods to evaluate and anonymize confidential micro-data sets. It includes all popular disclosure risk and perturbation methods. The package performs automated recalculation of frequency counts, individual and global risk measures, information loss and data utility statistics after each anonymization step. All methods are highly optimized in terms of computational costs to be able to work with large data sets. Reporting facilities that summarize the anonymization process can also be easily used by practitioners. We describe the package and demonstrate its functionality with a complex household survey test data set that has been distributed by the International Household Survey Network.

Suggested Citation

  • Templ, Matthias & Kowarik, Alexander & Meindl, Bernhard, 2015. "Statistical Disclosure Control for Micro-Data Using the R Package sdcMicro," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i04).
    • Handle: RePEc:jss:jstsof:v:067:i04
    DOI: http://hdl.handle.net/10.18637/jss.v067.i04
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    References listed on IDEAS

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    1. Krishnamurty Muralidhar & Rathindra Sarathy, 2006. "Data Shuffling--A New Masking Approach for Numerical Data," Management Science, INFORMS, vol. 52(5), pages 658-670, May.
    2. Krishnamurty Muralidhar & Rahul Parsa & Rathindra Sarathy, 1999. "A General Additive Data Perturbation Method for Database Security," Management Science, INFORMS, vol. 45(10), pages 1399-1415, October.
    3. Alfons, Andreas & Templ, Matthias, 2013. "Estimation of Social Exclusion Indicators from Complex Surveys: The R Package laeken," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 54(i15).
    4. Meyer, David & Hornik, Kurt, 2009. "Generalized and Customizable Sets in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 31(i02).
    5. Andreas Alfons & Stefan Kraft & Matthias Templ & Peter Filzmoser, 2011. "Simulation of close-to-reality population data for household surveys with application to EU-SILC," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(3), pages 383-407, August.
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    Cited by:

    1. Heng Xu & Nan Zhang, 2022. "Implications of Data Anonymization on the Statistical Evidence of Disparity," Management Science, INFORMS, vol. 68(4), pages 2600-2618, April.
    2. Wieringa, Jaap & Kannan, P.K. & Ma, Xiao & Reutterer, Thomas & Risselada, Hans & Skiera, Bernd, 2021. "Data analytics in a privacy-concerned world," Journal of Business Research, Elsevier, vol. 122(C), pages 915-925.

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