Data Anonymization: An Experimental Evaluation Using Open-Source Tools

Joana Tomás, Deolinda Rasteiro and Jorge Bernardino
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Joana Tomás: Institute of Engineering of Coimbra—ISEC, Polytechnic of Coimbra, Rua Pedro Nunes, 3030-199 Coimbra, Portugal
Deolinda Rasteiro: Institute of Engineering of Coimbra—ISEC, Polytechnic of Coimbra, Rua Pedro Nunes, 3030-199 Coimbra, Portugal
Jorge Bernardino: Institute of Engineering of Coimbra—ISEC, Polytechnic of Coimbra, Rua Pedro Nunes, 3030-199 Coimbra, Portugal

Future Internet, 2022, vol. 14, issue 6, 1-20

Abstract: In recent years, the use of personal data in marketing, scientific and medical investigation, and forecasting future trends has really increased. This information is used by the government, companies, and individuals, and should not contain any sensitive information that allows the identification of an individual. Therefore, data anonymization is essential nowadays. Data anonymization changes the original data to make it difficult to identify an individual. ARX Data Anonymization and Amnesia are two popular open-source tools that simplify this process. In this paper, we evaluate these tools in two ways: with the OSSpal methodology, and using a public dataset with the most recent tweets about the Pfizer and BioNTech vaccine. The assessment with the OSSpal methodology determines that ARX Data Anonymization has better results than Amnesia. In the experimental evaluation using the public dataset, it is possible to verify that Amnesia has some errors and limitations, but the anonymization process is simpler. Using ARX Data Anonymization, it is possible to upload big datasets and the tool does not show any error in the anonymization process. We concluded that ARX Data Anonymization is the one recommended to use in data anonymization.

Keywords: data anonymization; OSSpal methodology; ARX Data Anonymization tool; Amnesia (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
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