# Feedback-Based Integration of the Whole Process of Data Anonymization in a Graphical Interface
## 2 sdcApp
### Risk/Utility
#### 2.4.1 Risk Measures  
This part deals with information based on the risk scenario defined by the categorical key variables. Table 7 summarizes the choices that can be made. Selecting Information of risks by default displays the number and percentages of observations that have a higher individual re-identification risks [17] than the main part of the other records. This information is shown for both the initial as well as the anonymized dataset. The individual re-identification risk is computed based on the defined categorical key variables and reflects both the frequencies of the keys in the dataset and takes individual sampling weights into account if such have been specified. A record is said to have a re-identification risk different from the main part of the data if its personal re-identification risk is either larger than the median plus two times the Median Absolute Deviation of the distribution of all individual risks (a robust measure) or if it is deemed large. Furthermore, the number and percentage of expected re-identifications based on the individual risk approach for the original and modified dataset are displayed. At the top of the page, a group of radio buttons allows to change the results that will be shown. Selecting Risky observations allows filtering records in the anonymized dataset, depending on a threshold for the individual re-identification risk that can be selected using a slider input. Below this slider, the number and percentages of observations with individual re-identification risks larger than the currently specified threshold are shown and a table showing the units having larger individual risks than the current threshold are shown. The table contains categorical key variables as well as its individual risk value and numbers of fk and Fk that specify how often the combination of \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Feature** & **Description** \\ \hline Information of risks & shows the number of estimated re-identifications and the number of units with a higher risk than the main part of the other observations; allows comparison of individual risks in original and perturbed data \\ Suda2 risk measure & risk measure based on sample uniques for which also a subset of categorical key variables is unique in the (unweighted) micro-dataset \\ l-Diversity risk measure & searches for violations given a sensitive variable that needs to have at least \(l\) different values for each combination of key variables \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Risk measures and information for categorical key variables. characteristics of the key variable for the given unit occurs in the sample (fx) and how often the combination is estimated to occur in the population (Fk). Selecting Plot of risk using the radio button input field leads to two plots being shown. Both plots show histograms of individual re-identification risks. The upper plot shows the risks from the original, unmodified microdata while the second graph is based on the current state of the dataset and takes all previously applied SDC methods into account. The second option in the left-sided navigation bar--Suda2 risk measure-allows the user to apply the SUDA algorithm [24]. This algorithm can be used to search for Minimum Sample Uniques (MSU) in the data given the current set of categorical key variables. The algorithm looks at those records that are unique in the sample (sample uniques) and furthermore checks if any of these sample uniques are also special uniques. Special uniques are defined as records for which also a subset of the categorical key variables are sample unique. This algorithm can only be applied if the current problem instance has three or more categorical key variables specified. If the prerequisites are fulfilled, the user needs to choose a value for parameter disFraction by modifying a slider input field. This parameter refers to the sampling fraction for the simple random sampling or the common sampling fraction for stratified sampling used by the algorithm.