Anonymization and pseudonymisation are two of the techniques within what are known as masking actions, key in the context of data protection.

Data masking guarantees the security of sensitive information within databases, and at the same time allows companies to access the important source of value that this data constitutes. 

Thus, anonymization and pseudonymisation are essential to eliminate threats to data security in non-production environments (being one of the key actions of Test Data Management), cloud hosting and  data sharing processes. 

Beyond these contexts, anonymization and pseudonymisation are also important for compliance with the data protection law and the General Data Protection Regulation (GDPR).

At icaria Technology, we know that understanding both concepts and their importance involves knowing their definitions, similarities and differences. Therefore, we are sharing these key points in this brief guide on anonymization and pseudonymisation.

Anonymization

What does it involve?

Anonymization of data entails determining what information, within a database, identifies a person, and irreversibly and permanently deleting any possibility of obtaining that identification. 

When is it used?

Anonymization is used in databases with sensitive information for which recovery is not planned, or which must directly be completely deleted.

Banking, healthcare, legal or public administration sectors may thereby use the valuable information of a database for statistical purposes, or in software testing contexts, guaranteeing both data security and compliance with the law.

Pseudonymisation

What does it involve?

Pseudonymisation involves exchanging sensitive data identified within a dataset with synonyms or pseudonyms. Undertaking the process through a key, pseudonymisation is reversible, as it allows the original data to be recovered provided that the key is possessed.

Some pseudonymisation strategies include encryption with a secret key and breakdown into tokens.

When is it used?

As with anonymization, sectors such as banking, healthcare, legal or public administration may benefit from use of pseudonymisation. 

Nevertheless, the use of these techniques is more flexible, as it allows data security to be guaranteed, and at the same time allows recovery of sensitive information if it is required.

Anonymization vs pseudonymisation

Similarities

• Anonymization and pseudonymisation are two masking techniques and both are based on replacing sensitive information within a dataset. 
• At a legal level, anonymization and pseudonymisation are also considered valid techniques for guaranteeing data protection. 

Specifically, the Data Protection Law establishes that databases will only be valid if they comply with the principles of purpose and consent. In other words, personal data must be used for the purposes for which it was collected and in accordance with the consent granted by citizens. 

With this regulation, anonymization and pseudonymisation are recognised as two valid procedures for extending the use of data (for example, in the context of software testing) and maintaining regulatory compliance.

Differences

The main difference between anonymization and pseudonymisation is that the latter is reversible. 

Thus, in the case of pseudonymisation, information related to the data owner is not completely deleted, but instead replaced and protected. In turn, anonymisation is irreversible and permanent, removing any possibility of recovering information on the data owner, and therefore of identifying them.

Compliance with the GDPR regulation in test data environments

Compliance with the General Data Protection Regulation (GDPR) in test data environments constitutes a specific case within data protection, anonymization and pseudonymisation.

Firstly, it is necessary to understand the source from which the GDPR arises: it is a European regulation oriented toward guaranteeing the privacy of personal data. With the rise of Big Data, this regulation intends to broaden the fundamental rights of natural persons and to protect sensitive information from malicious attacks. 

Concepts such as the right to be forgotten, the right to portability and ARCO rights (access, rectification, cancellation and opposition) in turn arise from this law, which also establishes substantial fines in case of errors or negligence by companies.

Anonymization and pseudonymisation appear as valid tools in the context of this legislation. Thus, while the regulation defines personal data as “all information on an identified or identifiable natural person”, anonymisation and pseudonymisation allow masking and protection of the data so that it is not identifiable.

These actions are key in the context of non-production environments, as is the case of use of data in test environments. In this case, the use of Test Data Management tools is notable, which guarantees good practices in data processing.

The operation is simple: from systems such as TDM by icaria Technology it is possible to generate secure datasets which comply with the law and which are in turn complete, coherent and correct. 

To do so, it takes charge of identifying the sensitive data, and based on the needs of each company, applies the necessary processes of anonymization or pseudonymisation, all automatically.

Thus, companies gain the competitive advantage of having a completely secure and efficient database for use in test data environments for software and information analysis.

In turn, this automated process frees us human teams, saving time and reducing costs, while guaranteeing the quality of the tests. In other words, significantly better results are obtained with lower costs.

Are you looking for anonymization and pseudonymisation technology suitable for application in your test data? icaria Technology is the solution: request a demo and discover first hand how this tool can benefit your business.