Data Anonymization: An Experimental Evaluation Using Open-Source Tools

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.

[1]  Massimiliano Rak,et al.  A Conceptual Model for the General Data Protection Regulation , 2021, ICCSA.

[2]  Yuting Liang,et al.  Optimization-based k-anonymity algorithms , 2020, Comput. Secur..

[3]  Fabian Prasser,et al.  Flexible data anonymization using ARX—Current status and challenges ahead , 2020, Softw. Pract. Exp..

[4]  Fiza Abdul Rahim,et al.  A Comparative Study of Data Anonymization Techniques , 2019, 2019 IEEE 5th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS).

[5]  Naveen Choudhary,et al.  A Study on Models and Techniques of Anonymization in Data Publishing , 2019, International Journal of Scientific Research in Science, Engineering and Technology.

[6]  Azhar Rauf,et al.  Anatomization through generalization (AG): A hybrid privacy-preserving approach to prevent membership, identity and semantic similarity disclosure attacks , 2018, 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET).

[7]  Masahiro Mambo,et al.  Set-valued Data Anonymization Maintaining Data Utility and Data Property , 2018, IMCOM.

[8]  Jorge Bernardino,et al.  Open Source Data Mining Tools Evaluation using OSSpal Methodology , 2018, ICSOFT.

[9]  Josep Domingo-Ferrer,et al.  Comment on “Unique in the shopping mall: On the reidentifiability of credit card metadata” , 2015, Science.

[10]  Jorge Bernardino,et al.  Experimental Evaluation of Open Source Business Intelligence Suites using OpenBRR , 2015, IEEE Latin America Transactions.

[11]  Y. de Montjoye,et al.  Unique in the shopping mall: On the reidentifiability of credit card metadata , 2015, Science.

[12]  REGULATION (EU) 2019/518 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL , 2015 .