A Qualitative-Driven Study of Irreversible Data Anonymizing Techniques in Databases

Nowadays, privacy remains one of the most important challenges for the enterprises that handle personal data. Many mechanisms are widely used to tackle this challenge and make the use of Internet more secure and respectful of the privacy. For this aim, anonymizing data in database, by reversible or irreversible techniques, is one of such used mechanisms. Varieties of implementation of these techniques are provided and available, however, the choice of the suitable category and technique for a specific context is not an easy task. In this paper we focus on irreversible anonymizing category in database and we propose an approach that can help to make this choice easier based on classification according to criteria. Some of these last are well known on research fields and we define others related to the application context and data nature. As a result, the security officer could identify the most suitable technique to preserve privacy.