What is Data Pseudonymization?

Data Pseudonymization emerges as one of the data masking techniques used in the context of data protection.

With the Data Protection Law, new requirements arise for companies to manage data according to the law. Thus, this regulation sets conditions on the use that companies can give to users' personal data, which can only be used for the purposes for which they were collected and under the user's consent.

This would pose a significant limitation for businesses that may want to use data as a value source in processes like Business Intelligence or software testing.

Pseudonymization (along with other techniques such as anonymization) comes to solve this problem. Here's how.

What is the Pseudonymization?

Pseudonymization is a data masking technique where denominative (or sensitive) data are replaced by pseudonyms.

Thus, a sensitive attribute is substituted by another in such a way that it remains protected. Only through the use of a key or additional information is it possible to link the denominative data back with the related information.

Specifically, the General Data Protection Regulation (GDPR) includes a definition of pseudonymization as "the processing of personal data in such a manner that the data can no longer be attributed to a specific data subject without the use of additional information." This "additional information" must also "be kept separately and subject to technical and organizational measures to ensure non-attribution to an identified or identifiable person."

It thus creates a barrier to prevent individuals from being identified with their data collected in a set, ensuring that the data cannot be attributed to a physical person. This barrier, however, is not irreversible, as there is still the possibility of linking to the person if the key to reverse the process is known. However, for pseudonymization to be valid, the effort to obtain the key irregularly must be disproportionate.

In this sense, it is a more flexible masking technique than others like anonymization: on one hand, it ensures data security while, on the other hand, it allows for the original information to be recovered if necessary.

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Why Implement Pseudonymization

Many productive sectors, such as banking, healthcare, legal, or public administrations, are already applying pseudonymization to comply with the law and, at the same time, use data as a source of value.

It is thus an essential technique for data management and ensuring compliance with the law and associated rights, whose compliance is mandatory to avoid, in addition, the hefty penalties foreseen by the GDPR law (they can reach up to 4% of the annual turnover of the company or 20 million euros).

Through pseudonymization, it is considered that companies have taken appropriate measures for the protection of sensitive data, keeping them out of reach of unauthorized persons. In the case of software testing, the dissociation algorithms must be non-deterministic, and different applications, with different development cycles, need coherent but dissociated data at different times. While anonymization cannot offer this, pseudonymization does.

The law itself encourages companies to implement pseudonymization processes when it is the appropriate way to protect data and access the competitive advantage of having a secure database.

Techniques for Pseudonymization

Pseudonymization techniques vary depending on data use. For software testing, criteria include security and tester utility, ensuring data appears real while maintaining functional richness.

Real-looking Data Production

Non-deterministic algorithms specific to the information type generate realistically looking but fake data. For example, real emails are used to create consumable fake addresses.

Additional techniques include:

  • Secret Key Encryption: Encrypts personal data, reversible with the decryption key.
  • Hash Function: Generates a fixed-size alphanumeric output from inputs, summarizing information.
  • Deterministic Encryption or Hash Function with Erasure Key: Generates random numbers for data substitution, then erases the correlation table.
  • Key-Stored Function: Applies an extra input through a secret key for reversible pseudonymization.
  • Token Decomposition: Particularly useful in finance, replaces identifiable numbers with values based on encryption, sequence assignments, or random generation.

Implementing Pseudonymization

Manual pseudonymization is impractical for most databases. Automated software solutions ensure efficient law compliance.

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Solutions like icaria GDPR for personal data blocking and deletion, and icaria TDM for test data management, are pivotal. Contact icaria Technology for insights on leveraging these solutions for legal compliance and data value maximization.