What is anonymization, and how does it differ from pseudonymization?
The CNIL (French administrative regulatory body whose mission is to ensure that data privacy law is applied to the collection, storage, and use of personal data) describes data anonymization as a treatment that consists of using a set of techniques to make it impossible, in practice, to identify the person by any means and in an irreversible manner.
Pseudonymization, on the other hand, allows data to be used, but in a reversible manner. It is therefore subject to the GDPR (General Data Protection Regulation).
When we think about data in companies, we obviously think about the production environment. This environment hosts many applications used by end users, such as a bank advisor in a financial institution. Data in production is in most cases very secure and does not represent a major risk of leakage.
But sometimes it is necessary to take data out of those production environment to ensure the proper running of a business, and this is when cybersecurity issues arise.
This is the case, for example, in training environments for new employees, or for analysis purposes such as BI (Business Intelligence), which is now considered the nerve center of a company's marketing activities.
Other data are also extracted from the production environment and transferred to service providers, which are among the most targeted by attacks (targeting a service provider potentially allows hackers to affect hundreds of companies).
And of course, software development and testing environments, which need realistic test data.
All this data is exposed without being secured.
Anonymization addresses these issues by transforming it into fictional but realistic data. This data looks real to users but does not reveal any personal or identifying information. Any leakage of this data would therefore have no impact, since it is not real data.
But anonymization is not limited to these use cases. It can also be applied on a need-to-know basis.
Indeed, it is possible to integrate the anonymization mechanism into your tools and to use, or not, anonymization depending on the profile.
Let's imagine the case of a developer in charge of an HR application. In this case, he needs to have access to consistent data, but in no way realistic, since it is personal and identifying. Anonymization therefore meets this need. As for the business profile (Human Resources Manager), access to this data would remain unchanged.
Anonymization meets many use cases in cybersecurity. This is the reason why the efficiency of this technique also makes it possible to bypass the data protection legislation (GDPR), since the diffusion or reuse of anonymized data has no impact on the privacy of the persons concerned.