Meindl, B., & Templ, M. (2019). Feedback-Based Integration of the Whole Process of Data Anonymization in a Graphical Interface. Algorithms, 12(191). https://doi.org/10.3390/a12090191
The interactive, web-based point-and-click application presented in this article, allows
anonymizing data without any knowledge in a programming language. Anonymization in data
mining, but creating safe, anonymized data is by no means a trivial task. Both the methodological
issues as well as know-how from subject matter specialists should be taken into account when
anonymizing data. Even though specialized software such as sdcMicro exists, it is often difficult for
nonexperts in a particular software and without programming skills to actually anonymize datasets
without an appropriate app. The presented app is not restricted to apply disclosure limitation
techniques but rather facilitates the entire anonymization process. This interface allows uploading
data to the system, modifying them and to create an object defining the disclosure scenario. Once such
a statistical disclosure control (SDC) problem has been defined, users can apply anonymization
techniques to this object and get instant feedback on the impact on risk and data utility after SDC
methods have been applied. Additional features, such as an Undo Button, the possibility to export the
anonymized dataset or the required code for reproducibility reasons, as well its interactive features,
make it convenient both for experts and nonexperts in R—the free software environment for statistical
computing and graphics—to protect a dataset using this app.
en
Additional information:
Special Issue "Statistical Disclosure Control for Microdata" ; Special Issue Guest Editor: Templ, Matthias