# Feedback-Based Integration of the Whole Process of Data Anonymization in a Graphical Interface
## 2 sdcApp
### Anonymize
#### 2.3.4 Anonymization of Continuous Data  
The third section in the left-hand sidebar shows the methods that can be applied to numerical (key) variables. Table 6 summarizes the possible choices. We note that only the first choice (Top/Bottom coding) is always listed; the remaining choices only appear if at least one variable has been set as continuous key variable when defining the problem, as discussed in Section 2.3.1. Selecting Top-/Bottom-Coding enables the user to replace values that are above (top-coding) or below (bottom-coding) a threshold with a different number, which is typically the threshold itself. In the GUI, the user needs to select a variable from a drop-down field that should be re-coded. This field only allows to select continuous variables in order to prevent the application of the method to categorical variables. Then it must be set if top- or bottom coding should be performed using a radio button input field. Furthermore, two numbers--the threshold value and the replacement value--must be set. To help users find suitable threshold values, a box plot showing the distribution of the currently \begin{table}
\begin{tabular}{l l} \hline \hline
**Feature** & **Description** \\ \hline Top/Bottom coding & replace values above or below a selected threshold with a given value \\ Microaggregation & groups records and replaces values of a numeric variable for each group \\  & with a summary statistic \\ Adding noise & adds stochastic noise to variables \\ Rank swapping & randomly swaps/exchanges values within a given range \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Anonymization methods for continuous variables. selected variable is shown below the input controls. After all inputs have been set, additional elements appear between the input fields and the box plot. The first additional element is a text stating how many of the values would be replaced as well as the corresponding percentage. Below this information, a button appears. Once this button is pressed, the values are replaced according to the current settings and the page refreshes. The box plot is updated as well as the right hand sidebar. If the selected and re-coded variable was defined as a numeric key variable, the values referring to risk in numerical key variables and information loss measures change in the sidebar. Microaggregation [18] is a method in which records are grouped based on a proximity measure of variables of interest. In a second step, for each group, a summary statistic (aggregate value), which is typically the arithmetic mean, is computed and then released for all records in the group, instead of the original, individual record values. This method reduces the variation of a continuous key variable and additionally protects against (direct) matching. It also can be interpreted as \(k\)-anonymity for continuous variables. In sdcMicro, non-cluster-based and cluster-based methods are distinguished. Users can choose from a total of eight non-cluster-based and four cluster-based methods. The grouping can also be can nonrobust (for example, the default algorithm Maximum Distance to Average Vector (MDAV) [3]) or robust such as the Robust Maximum Distance (RMD) algorithm [19]. If the users chooses to apply microaggregation to numeric key variables, the first choice is to select one of the available methods from a drop-down menu. The user also needs to specify an aggregation level using a slider input. This value refers to the size of the groups that will be formed by the algorithm. Further options that can be made include the selection of a subset of numerical key variables for which should be microaggregated (by default, all key variables will be used) or if the algorithm should be applied independently given the characteristics of a stratification variable that needs also be chosen in this case. Figure 3 features a screen shot how the RMD algorithm may be specified for some continuous key variables without stratification for a group size of 3. Additionally, question mark icons are shown next to most input fields.