# Data Anonymization: An Experimental Evaluation Using Open-Source Tools
## 6 Tool Assessment Using a Real Dataset
### Evaluating Pfizer Vaccine Tweets Dataset with ARX Data Anonymization  
In this section, the Pfizer Vaccine Tweets dataset is inserted into the ARX Data Anonymization Tool. After creating the project, the dataset is inserted into the tool. The file format chosen is CSV and the delimiter is semicolon. After verifying that every data type is correct, the dataset is uploaded, and it is displayed as shown in Figure 4. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Categories** & **ARX Data Anonymization** & **Amnesia** \\ \hline Functionality & 0.6 & 0.3 \\ Operational Software Characteristics & 1 & 0.8 \\ Support and Services & 0.4 & 0.4 \\ Documentation & 0.3 & 0.3 \\ Software Technology Attributes & 0.6 & 0.45 \\ Community and Adaption & 0.2 & 0.2 \\ Development Process & 0 & 0 \\
**Total** & **3.1** & _2.45_ \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Category Results. \begin{table}
\begin{tabular}{c c} \hline \hline
**Attribute** & **Attribute Type** \\ \hline id & Sensitive \\ username & Identifier \\ userlocation & Quasi-Identifier \\ userdescription & Quasi-Identifier \\ usercreated & Non-sensitive \\ userfollowers & Non-sensitive \\ userfriends & Non-sensitive \\ userfavourites & Non-sensitive \\ userverified & Non-sensitive \\ date & Non-sensitive \\ text & Non-sensitive \\ hashtags & Non-sensitive \\ source & Non-sensitive \\ retweets & Non-sensitive \\ favorits & Non-sensitive \\ isretweet & Non-sensitive \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Pfizer Vaccine Tweets Dataset Attribute Types. Afterwards, the attribute types need to be selected according to the information in Table 4. The privacy model needs to be set for the sensitive attributes. The tool selects the privacy models l-Diversity, t-Closeness, \(\delta\)-Disclosure privacy, and \(\beta\)-Likeness. The one chosen is the l-Diversity with an l value of 5, as shown in Figure 5. The value 5 is chosen because if the value is too high data utility is lost, and considering the size of the dataset, 5 is the appropriate value. After that, it is necessary to create the hierarchies for the identifier and quasi-identifier attributes. For the username attribute, the hierarchy type chosen is Masking with the masking character *. The Masking type was chosen because the username attribute is a text attribute and as the purpose of this hierarchy is to hide the information, this is the correct type to use since it is going to replace the attribute values with the masking character. The Max characters value chosen is 6 because in Amnesia this is the value used. In Amnesia, this size is the number of characters that all the values for this attribute have in the hierarchy. In ARX, this is not the case, but we use the same value, and it is also used when there is a cell without a value in the dataset. The result for this hierarchy can be seen in Figure 6, where it is possible to verify that as we go up in the level, a masking character is added to the attribute value, which means that this is going to have the same number of levels as the number of characters of the biggest attribute value. The special character asterisk (*) is the masking character chosen, which means that in each level of the hierarchy, a character of the original value is going to be replaced by this special character. As it is possible to verify in Figure 6, in the Level-1 was added a masking character, in Level-2 was added two masking characters, and it goes like this until the end of the hierarchy. The hierarchy has as many levels as the number of characters of the largest original value in the dataset for this attribute. At the last level of the hierarchy, the largest value needs to be replaced entirely with asterisks. Figure 4: Dataset uploaded in ARX Data Anonymization. Figure 5: Privacy Model. The next hierarchy created is for userlocation attribute.