# Feedback-Based Integration of the Whole Process of Data Anonymization in a Graphical Interface
## 2 sdcApp
### Anonymize
#### 2.3.4 Anonymization of Continuous Data
By hovering over these icons, additional information is shown in a pop-up window, which will facilitate users to find appropriate parameter settings. For some methods, several additional inputs are shown. For example, when using cluster-based methods, it is possible to select the clustering algorithm (such as \"kmeans\",  
Figure 3: Screen to set up microaggregation in the GUI. \"cmeans\", or \"bclust\") as well as possible data transformations (\"log\", \"box-cox\", or \"none\") that should be applied before the cluster algorithm is applied. For some non cluster-based methods, it is possible to choose the aggregation statistics (such as the mean or the median). Once all required options have been set, an action button appears below all inputs. Clicking on this button performs the microaggregation of the selected variables according to the options that have been set. As the computation might take some time, on the bottom right screen, a progress bar showing that the process is running appears. Once the algorithm has finished, the page updates and the \"Show summary\" page is displayed. On this page, all measures depending on numerical key variables have been recomputed and show current values. Another popular way to mask numerical variable is to overlay original numbers with a stochastic noise distribution that has typically a mean of 0. Selecting Adding Noise allows to modify numerical key variables with noise. Users can select one or more numerical key variable from a input field which is by default empty. If it is left empty, all numerical key variables will be perturbed. Next to the variable selection field, a single specific algorithms needs to be selected. While some of the algorithms (e.g., \"additive\") add noise completely at random to each variable depending on its size and standard deviation, other methods are more sophisticated. Algorithms \"correlated\" and \"correlated2\" [20] try to preserve the covariance structure of the input variables. Method \"ROMM\" [21] is an implementation of the Orthogonalized Matrix Masking method) and algorithm \"outdect\" [19] adds noise only to outliers. These outliers are identified by univariate and robust multivariate procedures based on a robust Mahalanobis distance which is computed by the MCD estimator [22]. Below these inputs, the amount of perturbation needs to be specified by changing the value of a slider input. This input is dynamically labeled depending on the previous choice of the method. Since the parameterization for the different methods is different, this slider has different default values and different ranges depending on the choice of the method. If all options have been set, pressing a button that is shown at the bottom of the page finally applies the stochastic noise to the selected variables. Once the algorithm finished, the page view changes to the Show summary page, where again all relevant measures were recalculated. The last method that can be applied to numerical key variables is rank swapping [23]. The main idea of this algorithm is to swap values within a given range so that the correlation structure of the original variables is (mostly) preserved and some perturbation is added to the variables. After specifying the numerical key variables to which the algorithm should be applied, the parameters for the procedure needs to be specified using five different slider inputs. Two sliders are used to define percentages of values to be bottom- or top-coded before the algorithm starts, which allows to deal with outlying observations. The sliders named \"Subset-mean preservation factor\" and \"Multivariate preservation factor\" allow further fine-tuning of the algorithm. The first one defines how the maximum percentage means before and after the perturbation are allowed to differ, while the second slider specifies a limit on how much correlations between non-swapped and swapped variables are allowed to differ.