# Data Anonymization: An Experimental Evaluation Using Open-Source Tools
## 2 Related Work  
This section presents some of the most important works related to anonymization techniques and anonymization tools. In their work, [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [5] extended the open-source ARX Data Anonymization Tool in a way that it could apply a full-domain generalization algorithm in different subsets. The idea was to implement horizontal and vertical partitioning strategies so that the tool could work with more flexible transformation models while preserving scalability. With these modifications, the ARX Data Anonymization Tool was able to support four different transformation methods: generalization, suppression, sampling, and micro-aggregation. The referred authors tested these modifications using six real-world datasets and compared the results with UTD Anonymization Toolbox, with the Mondrian algorithm, and against the [PERSON] et al. algorithm [6; 7]. The goal of this comparison is to prove that the ARX Data Anonymization Tool has better results for scalability and data utility when using local generalization with horizontal and vertical partitioning strategies enabled. In comparison with UTD Anonymization Toolbox, the ARX Data Anonymization Tool has better results in data utility and scalability. Comparing the new implementation with the [PERSON] et al. algorithm [6; 7], the ARX Data Anonymization Tool has better results in terms of data utility, but in terms of scalability, the [PERSON] et al. algorithm [6; 7] has better results. This could be justified by the fact that the new algorithm always guarantees identical records in input data but also the output data, so this algorithm is less flexible. [PERSON] and [PERSON] [8] realized that, with the usage of the existing data anonymization algorithms, when several modifications were applied to the database, data utility and data properties were considerably reduced. Therefore, they tried to create a new schema to anonymize data called sibling suppression. The main goal of this anonymization approach is to reduce data utility lost and maintain the data properties, such as database size and the number of records. This schema is applied in two steps: in the first step, the records need to be grouped based on the adversary knowledge. In the second step, the items grouped before need to be replaced by a surrogated item, which is an item of the same category of the items in the adversary knowledge based on the hierarchy tree. To evaluate this new approach, they used a real-world dataset (BMS-WebView2), the Normalized Certainty Penalty (NCP) metric to measure the information loss, and the dissimilarity metric to measure the difference between the original database and the anonymized database so that they could compare the data property results. The results were positive for this new schema because the dissimilarity was zero. Thus, the number of records of the original database is the same as the number of records of the anonymized database, since this schema works in the selection and replacement of the items. They also have low values in the result for information loss, which means they did not lose data in the anonymization process. Another advantage they refer to in the paper is that data utility is preserved in data mining tasks. [PERSON], [PERSON], [PERSON], and [PERSON] [9] compared some anonymization techniques using the same dataset. The purpose of the study was to review the strengths and weaknesses of each one of the techniques. The techniques studied were generalization, suppression, distortion, swapping, and masking. They identify the best techniques that should be used for each one of the attributes of the dataset, considering the data type of the attribute. They verified that the masking and distortion techniques were the ideal for all types of data, suppression has the same results as masking but with more efficiency, and distortion makesthe data become unrecognized but can be reverted to original data by removing the noise. The conclusion is that many techniques can be applied to any data.