Data Anonymization: Discover the Most Notable Techniques

Data anonymization has become a crucial process in a context where large volumes of data, many of which are sensitive or classified as personal information, are handled.

Numerous companies and organizations across a variety of industries collect and store large amounts of data. All of them are obliged by the General Data Protection Regulation (GDPR), and therefore, must apply data anonymization techniques at all stages of the sensitive data lifecycle.

We therefore analyze what data anonymization is and how to implement it in the most efficient way.

What is Data Anonymization?

Data anonymization includes all processes aimed at irreversibly and permanently removing personally identifiable information from a data set.

These efforts include both the elimination and the generation of audits regarding the exploitation of such data, so that it's known who uses them, when, and for what purpose.

The goal of these processes is to minimize the risk of identifying individuals within that set. This protects the privacy and confidentiality of the data, making it impossible for people to be identified from them.

In a context where data has become one of the key assets for many companies, data anonymization is fundamental. Thus, it ensures that data collected and processed for research, analysis, or publication purposes do not compromise the identity of the individuals involved.

Good practices in data anonymization follow the premise of proactivity and security by design, taking security measures from the beginning and before problems arise.

Data Anonymization vs. Pseudonymization

Understanding both data anonymization and pseudonymization allows for a deeper comprehension of both processes. Briefly defined, pseudonymization can be described as the process of exchanging sensitive data within a data set for synonyms or pseudonyms. This process is encrypted by a key, which allows the original data to be recovered. Thus, sensitive information is replaced and protected.

The main difference between pseudonymization and data anonymization is that the former creates a reversible process: as long as the decryption key or equivalent mechanism is available, access to the data is possible. The result is two data sets: one with pseudonymized information and another with the real information.

Conversely, data anonymization generates a unique and new data set in an irreversible process.

Why is Data Anonymization Important?

The primary reason driving companies and organizations to apply the technique of personal data anonymization is compliance with legality.

In the European context, the GDPR appears, a regulation aimed at protecting the privacy of citizens' personal data and which contemplates data anonymization to comply with these legal requirements.

Furthermore, data anonymization is a key step in reducing security risks, minimizing the chances of confidential or sensitive information theft. This is vital in building trust among citizens, facing reputation crises due to GDPR non-compliance and data security breaches.

Main Data Anonymization Techniques

Data Masking

Removes personal identifiers from the data set, either partially (keeping some data that needs to be used) or by quarantining the data (for example, working with reference numbers).

Synthetic Data Generation

Creates fictitious or artificial data that maintains certain characteristics and patterns of the original data but does not contain personally identifiable information (PII).

Data Perturbation

Adds noise to data sets or modifies data values in a controlled manner. This changes the values of the original data so that the results obtained from them are valid and useful for analysis and processing, but at the same time, it is difficult or impossible to associate those data with specific individuals.

Data Swapping

Changes the order or position of elements in a data set. This introduces a random distortion while preserving the structure of the original data, which remains present but not in their original records.

icaria TDM: The Tool for Data Anonymization

icaria TDM emerges with the goal of ensuring GDPR compliance in pre-production environments, such as testing environments. Thus, it facilitates the processes of data anonymization, mass dissociation, and data segmentation, generating complete, coherent, and correct data sets for testing.

It thus becomes a key tool for the QA tester, allowing access to the competitive advantage of having a secure, effective, and legally compliant database in testing data environments. All this, moreover, freeing human teams and reducing associated costs through automation.

If you are looking for data anonymization technology aimed at test data, icaria Technology has the solution: request a demo of icaria TDM and check for yourself the advantages of this software for the personal data anonymization technique.