# Feedback-Based Integration of the Whole Process of Data Anonymization in a Graphical Interface
## 2 sdcApp
### Anonymize
#### 2.3.1 Defining a SDC Problem  
The layout of this page is split into two parts and can be accessed by clicking on Anonymize in the top navigation bar. On the left hand side, an interactive table is shown that needs to be modified in order to define a new problem instance. On the right hand side, the user is given the possibility to select a single variable from the dataset for further exploration, which is shown in Figure 2. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Name** & **Description** \\ \hline _Display microdata_ & gives an overview of the data and features an interactive, sortable table \\ \hline _Explore variables_ & returns for one or two selected variables both appropriate plots and summary statistics as well as information about the amount of missings \\ \hline _Reset variables_ & allows to “undo” all modifications to selected variables \\ \hline _Use subset of microdata_ & the size of the dataset can be reduced for testing purposes which is useful because some of the SDC methods run faster on smaller data \\ \hline _Convert numeric to factor_ & allows to create a factor from a continuously scaled variable, e.g. re-coding age in years into age groups \\ \hline _Convert variables to numeric_ & this feature allows to re-code variables that are stored as R factors or character vectors (numbers encoded as characters) to numbers \\ \hline _Modify factor variable_ & allows to combine and rename levels of an existing factor variable \\ \hline _Create a stratification variable_ & some SDC methods can be applied independently on subgroups of the dataset; on this page, variables defining such groups can be generated \\ \hline _Set specific values to M_ & sometimes it is desirable to delete (or rather set to missing) specific values in the micro-dataset before starting the anonymization process \\ \hline _Hierarchical data_ & in case a hierarchical dataset (an example would be persons living in households) needs to be anonymized, this feature allows to prepare a combined dataset by uploading a file for the upper hierarchy (households) and merging it to the lower level file previously uploaded by specified identification variables \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Interactive features to update, view, and microdata. The exploration of variables on the same page is useful to decide which variables should be used as key variables. Below, the variable selection dropdown field, a suitable graph depending on the scale of the selected variable is shown. Below the plot (not shown in Figure 2), summary statistics such as the number of unique values including missing instances of (NA) or a 6-number summary in case of continuous variable are shown while for non-numeric variables a frequency table is displayed. The columns of the interactive table that consists of a single row for each variable from the micro-dataset are listed in Table 3. The page also features two slider inputs labeled Parameter \"alpha\" and Parameter \"seed\". The latter is relevant for specifying an initial start value for the random number generator which is useful if results should be reproducible. The former parameter specifies how scMicro should deal with missing values in categorical key variables. Details about this parameter can be found in the help-page of the freqCalc-function. In column Key variables, radio buttons indicate if a variable should be used as categorical or numerical key variable, which is by default not the case for any variable. However, multiple variables  
Figure 2: Defining a problem instance: on the left hand side important (key) variables can be defined, while a suitable graphic on the right hand side helps to identify suitable variables.