Publications

The principles, methods, algorithms and data structures underlying the ARX anonymization tool have been published in peer-reviewed journals and conference proceedings:

  • Fabian Prasser, Florian Kohlmayer, Helmut Spengler, Klaus A. Kuhn.
    A Scalable and Pragmatic Method for the Safe Sharing of High-Quality Health Data.
    IEEE Journal of Biomedical and Health Informatics, March 2017. [epub ahead of print]
    (Link)
  • Fabian Prasser, Johanna Eicher, Raffael Bild, Helmut Spengler, Klaus A. Kuhn.
    A Tool for Optimizing De-Identified Health Data for Use in Statistical Classification.
    Accepted for the 30th IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS 2017).
  • Johanna Eicher, Klaus A. Kuhn, Fabian Prasser.
    An Experimental Comparison of Quality Models for Health Data De-Identification.
    Accepted for the 16th World Congress on Health and Biomedical Informatics (MedInfo 2017).
  • Fabian Prasser, Raffael Bild, Johanna Eicher, Helmut Spengler, Florian Kohlmayer, Klaus A. Kuhn.
    Lightning: Utility-Driven Anonymization of High-Dimensional Data.
    Transactions on Data Privacy 9:2 (2016) 161 – 185, August 2016. (Link)
  • Fabian Prasser, Raffael Bild, Klaus A. Kuhn.
    A Generic Method for Assessing the Quality of De-Identified Health Data.
    Proceedings of MIE 2016 / Studies in Health Technology and Informatics, Volume 228: Exploring Complexity in Health: An Interdisciplinary Systems Approach, IOS Press, August 2016. (DOI)
  • Fabian Prasser*, Florian Kohlmayer*, Klaus A. Kuhn.
    The Importance of Context: Risk-Based De-Identification of Biomedical Data.
    Methods of Information in Medicine, Schattauer, June 2016. (DOI)
  • Fabian Prasser, Florian Kohlmayer, Klaus A. Kuhn.
    Efficient and Effective Pruning Strategies for Health Data De-Identification.
    BMC Medical Informatics and Decision Making, April 2016. (DOI)
  • Fabian Prasser*, Florian Kohlmayer*.
    Putting Statistical Disclosure Control Into Practice: The ARX Data Anonymization Tool.
    In: Gkoulalas-Divanis, Aris, Loukides, Grigorios (Eds.): Medical Data Privacy Handbook, Springer, November 2015. ISBN: 978-3-319-23632-2. (DOI)
  • Florian Kohlmayer*, Fabian Prasser*, Klaus A. Kuhn.
    The Cost of Quality: Implementing Generalization and Suppression for Anonymizing Biomedical Data With Minimal Information Loss.
    Journal of Biomedical Informatics, October 2015. (DOI)
  • Fabian Prasser*, Florian Kohlmayer*, Ronald Lautenschlaeger, Klaus A. Kuhn.
    ARX – A Comprehensive Tool for Anonymizing Biomedical Data.
    Proceedings of the AMIA 2014 Annual Symposium, November 2014, Washington D.C., USA. (Pubmed)
  • Fabian Prasser*, Florian Kohlmayer*, Klaus A. Kuhn.
    A Benchmark of Globally-Optimal Anonymization Methods for Biomedical Data.
    Proceedings of the 27th IEEE International Symposium on Computer-Based Medical Systems, May 2014, New York City, USA. (DOI)
    Authors' version
  • Florian Kohlmayer*, Fabian Prasser*, Claudia Eckert, Klaus A. Kuhn.
    A Flexible Approach to Distributed Data Anonymization.
    Journal of Biomedical Informatics, December 2013. (DOI)
  • Florian Kohlmayer*, Fabian Prasser*, Claudia Eckert, Alfons Kemper, Klaus. A. Kuhn.
    Flash: Efficient, Stable and Optimal K-Anonymity.
    Proceedings of the 4th IEEE International Conference on Information Privacy, Security, Risk and Trust (PASSAT), September 3 – 5, 2012, Amsterdam, Netherlands. (DOI)
    Authors' version with minor corrections
  • Florian Kohlmayer*, Fabian Prasser*, Claudia Eckert, Alfons Kemper and Klaus A. Kuhn.
    Highly Efficient Optimal K-Anonymity For Biomedical Datasets.
    Proceedings of the 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS), June 2012. (DOI)
    Authors' version with minor corrections

* Both authors contributed equally to this work.