IDEAS home Printed from
   My bibliography  Save this article

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


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


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

    Download full text from publisher

    File URL:
    Download Restriction: no

    File URL:
    Download Restriction: no

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. 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.
    2. 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.
    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).
    Full references (including those not matched with items on IDEAS)

    More about this item


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jss:jstsof:v:067:i04. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.