# Feedback-Based Integration of the Whole Process of Data Anonymization in a Graphical Interface
## 1 Introduction
### Outline and Brief Comparison of Graphical User Interfaces for SDC  
One of the first graphical user interfaces was provided via the software \(\mu\)-Argus [3]. The software is still developed and maintained by Statistics Netherlands and other partners, and some of those extensions are being subsidized by Eurostat. The software features a graphical pointand click user interface, which was based on \"Visual Basic\" until version 4.2., and is now (Version 5.1. and onwards) written using \"Java\" and can be downloaded for free from the CASC website ([[http://neon.vb.cbs.nl/casc/mu.htm](http://neon.vb.cbs.nl/casc/mu.htm)]([http://neon.vb.cbs.nl/casc/mu.htm](http://neon.vb.cbs.nl/casc/mu.htm))). Currently, only 32-bit versions have been built, and there is no command line interface available. The tool uses a certain range of different statistical anonymization methods such as global re-coding (grouping of categories), local suppression, randomization, adding noise, microaggregation and top- and bottom-coding. A number of tools with a graphical user interface are available for basic frequency calculation and for ensuring \(k\)-anonymity, \(l\)-diversity, and similar frequency calculations. TIAMAT [4] is a visual tool allowing data publishers to select a suitable \(k\)-anonymization transformation and its corresponding parameters in order to protect their data. Since it does not support additional methods, it is not considered in the following. This is also true for PARAT (version 6) that is based on frequency calculations only. OpenAnonymizer is based on the concepts of \(k\)-anonymity and \(l\)-diversity only, and thus will not considered further. Also, SECRETA [5] is not considered because of its limited features; for example, there is no internal risk estimation available in this tool. Amnesia [6] (version 1.0.6) is a data anonymization tool developed at the Athena Research Center. Amnesia has an hierarchy creator and editor for the anonymization. However, it supports \(k\)-anonymity and \(k^{m}\)-anonymity only. Thus, it is no longer considered here. The Cornell Anonymization Toolkit [7] provides \(l\)-diversity and \(t\)-closeness and is also not further investigated, because, as for all the other mentioned tools, in this paragraph, the following toolkit Arx [8] has basically implemented these methods and provides a well-developed GUI as well. Arx [8] is implemented in Java (current version 3.7.1), and is mainly used for biomedical data without special data structures. The anonymization in Arx consists of three basic steps: first, configure the anonymization process; second, explore the so-called \"solution space\"; and, last, analyze the perturbed data. The tool is useful for \(k\)-anonymity, \(l\)-diversity, and similar approaches such as \(t\)-closeness or \(\delta\)-presence [9]. It provides interactive features to investigate in the information loss based on univariate summaries of the original and perturbed data. Arx it has more methods integrated than other tools in the biomedical area. However, it cannot deal with data from complex designs and has limited features apart from pure frequency-based procedures. sdcApp[1] is the shiny-based [10] GUI on top of the (command line) package sdcMicro[1]. It accesses methods from sdcMicro and provides a bunch of useful features for anonymization of data. It can not only deal with biomedical registers, but also includes methods for complex samples. We note that a previous version of the GUI, sdcMicroGUI [11], is not comparable with sdcApp. sdcApp is a new implementation, written from scratch with new ideas, workflows, methods, appearance, and a new concept. The features of sdcApp are briefly outlined in the Section 1.2. Table 1 gives an overview of software and available methods of three GUIs for data anonymization: -\(\mu\)-Argus [3] from Statistics Netherlands, the sdcApp of the R package sdcMicro[1], and Arx [8]. We note that sdcApp provides the most comprehensive list of popular methods.