Software

The methods used in this book are exclusively available in the software environment R (R Development Core Team 2014). A very brief introduction to some functionalities of R is given. This introduction does not replace a general introduction to R but shows some points that are important in order to understand the examples and the R code in the book. The package sdcMicro (Templ et al. 2015) forms a basis for this book and it includes all presented SDC methods. It is free, open-source and available from the comprehensive R archive network (CRAN). This package implements popular statistical disclosure methods for risk estimation such as the suda2-algorithm, the individual risk approach or risk measurement using log-linear models. In addition, perturbation methods such as global recoding, local suppression, post-randomization, microaggregation, adding correlated noise, shuffling and various other methods are integrated. With the package sdcMicro, statistical disclosure control methods can be applied in an exploratory, interactive and user-friendly manner. All results are saved in a structured manner and these results are updated automatically as soon a method is applied. Print and summary methods allow to summarize the status of disclosure risk and data-utility as well as reports can be generated in automated manner. In addition, most applications/anonymizations can be carried out with the point-and-click graphical user interface (GUI) sdcMicroGUI (Kowarik et al. 2013) without knowledge in the software environment R or the newer version of sdcMicroGUI, an app that is available within the package sdcMicro as function sdcApp. The new version runs in a browser and is based on shiny (Chang et al. 2016). A software package with a similar concept as sdcMicro—the simPop package (Templ et al. 2017)—is used to generate synthetic data sets.