Data Anonymization: An Experimental Evaluation Using Open-Source Tools

被引：13

作者：

Tomas, Joana ^{[1
]}

Rasteiro, Deolinda ^{[1
]}

Bernardino, Jorge ^{[1
,2
]}

机构：

[1] Polytech Coimbra, Inst Engn Coimbra ISEC, Rua Pedro Nunes, P-3030199 Coimbra, Portugal

[2] Univ Coimbra, CISUC, Ctr Informat & Syst, Polo 2, P-3030290 Coimbra, Portugal

来源：

FUTURE INTERNET | 2022年 / 14卷 / 06期

关键词：

data anonymization; OSSpal methodology; ARX Data Anonymization tool; Amnesia;

D O I：

10.3390/fi14060167

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页数：20