Data Masking vs. Pseudonymization: How do they differ?

Safeguarding personal data has always been one of the building blocks and a major reason for the creation and development of GDPR. The GDPR requirements, especially that of protecting personal data, are clearly outlined, but the execution with minimal hurdles is the challenge to be addressed. The market has plenty of tools to redact (anonymize) this data, but that is not what will be covered in this blog. One challenge might arise is what to redact and how to redact it. Types of anonymization are numerous such as redaction, anonymization, pseudonymization, data masking, data obscuring, etc. However, these terms are not synonymous and understanding the difference is key to achieving compliance. Knowing what is appropriate for your use case is to understand the difference between Data Masking and Pseudonymization which are often used interchangeably.

What is Data Masking

Data masking, which is also known as data obfuscation, is the altering of original data in a way that the true values are hidden, i.e., data masking a car's plate number.

The 2 types of Data Masking:

1. Static Data Masking (SDM): Performed on a copy of the data.
2. Dynamic Data Masking (DDM): Applies masking in real-time i.e., video

In this blog, we will only talk about the Static Data Masking (SDM), to be able to have a 1:1 comparison with pseudonmyization which is only done on static data.

What is Pseudonymization?

Pseudonymization is the replacement of private identifies (personal data) with fake ones, for instance a person's initials when replacing a person's name. It is essentially redacting/anonymizing the data with an extra layer, known as the identifier or filler words. For example, replacing a patient's name "John Smith" with a randomly generated identifier, such as "XY12345Z".

Key Differences

Purpose and Method:

• Data Masking: Alters the original data to create a structurally similar but inauthentic version, making it unusable for those without authorization.
• Pseudonymization: Replaces identifying fields within a data set with one or more artificial/fake identifiers, or pseudonyms.

Reversibility:

• Data Masking: It is generally irreversible, meaning that it cannot be changed back to its original state. However, Data masking can be reversed with a key or algorithm depending on the technique used, with simpler methods allowing authorized access to the original data, while complex methods like encryption aim for permanent anonymization which is irreversible.
• Pseudonymization: Provides the choice between reversible and irreversible. It is reversible provided you have access to the mapping of the pseudonyms to their original words; which is done prior to the processing of data. Pseudonymization is irreversible otherwise.

Compliance and Legal Considerations:

• Data Masking: Often used in non-production environments, such as development and testing, where there is a need to work with data that is structurally similar to the original but without exposing sensitive information.
• Pseudonymization: Recognized under privacy regulations like the GDPR as a measure that can reduce risks to the data subjects and help maintain the data controllers' and processors' compliance with data protection obligations.

Conclusion

Data masking and Pseudonymization are similar yet very different, the difference lying mainly in their use cases and how they safeguard the data. Data Masking is predominantly used in testing environments like in IT for instance, where a tool needs to be tested with data but that data doesn't have to be real or valid like credit card details. On the other hand, Pseudonymization offers a solution both for protecting the data and maintaining a document's readability through the use of pseudonyms for analytical reasons. At NAIX GmbH, we pseudonymize data, thus effectively being compliant under GDPR law.

Would you like to learn more on how we can pseudonymize your data?

• Book a Demo with Ray: carried out in English or French
• Book a Demo with Patricia: carried out in German