# Feedback-Based Integration of the Whole Process of Data Anonymization in a Graphical Interface
## 2 sdcApp
### Risk/Utility
#### 2.4.2 Visualizations  
The second block in the left-hand sidebar allows to either compare current key variables in the original and anonymized dataset graphically or using summary statistics presented in tables. It is also possible to view measures of information loss based on re-coding and display the number of observations violating \(k\)-anonymity for arbitrary values of \(k\). Table 8 summarizes the possibilities.  
The first two choices Barplot/Mosaicplots and Tabulations allow to compare the distribution of categorical key variables. For both cases, the user has to select one or two categorical key variables  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Feature** & **Description** \\ \hline Barplot/Mosaicplots & visual display of categorical key variables in its initial and current state \\ Tabulations & tabulations of categorical key variables for original and current state \\ Information loss & shows information loss measures due to re-coding \\ Obs. violating k-anon & Number and Percentage of observations violating \(k\)-anonymity \\ \hline \hline \end{tabular}
\end{table}
Table 8: Graphical and tabular comparison of anonymized vs. original microdata.  
from drop-down input fields in which only the categorical key variables can be selected. For the former case, two plots appear below the inputs, whereas in the later case, two tables are shown next to each other below the select input fields. Both the graphs and the tables are automatically updated if the selection of key variables changes.  
If only one variable is specified and the value of the second variable selection input field is None, the user is either shown a bar plot showing the number of occurrences for each level of the input variable or a simple frequency table showing exactly the same information. In case two variables have been selected, either a mosaic plot or a cross-tabulation of the two selected variables for both the original and the anonymized variables is shown.  
Selecting Information loss shows measures of information loss that are due to re-coding (or rather combining) levels of categorical key variables, thus removing information from the data. Here, users are presented with an interactive table that can be sorted by clicking on the small arrow signs that are shown next to the column names. The table includes, for each categorical key variable, the name of the key variable, the number of levels, and the mean and minimum size of categories for the original and the modified variables.  
The next feature is to find out how many records in the anonymized dataset violate \(k\)-anonymity for different choices of parameter \(k\). By clicking on Obs violating k-Anon, the user is presented with a slider input that refers to parameter \(k\). Dragging the slider with the mouse or changing the value of the slider with the arrow keys on the keyboard leads to a recalculation of the number and percentage of observations that violate the \(k\) anonymity measure. This information is printed on the screen below the slider. Additionally, a table listing all the observations in the dataset violating \(k\)-anonymity is shown. For these observations, the interactive table contains all categorical key variables as well as the estimated individual risks (in column risk) as well as parameters fk and Fk as discussed in Section 2.4.1.