GDPR Anonymization and Pseudonymization Policy Template

by avinash v


The GDPR Anonymization and Pseudonymization Policy set out the requirements for anonymizing and pseudonymizing personal data under the GDPR. Anonymization is the process of removing all personal data from a dataset. Pseudonymization is the process of replacing all original data with pseudonyms.

GDPR Anonymization and Pseudonymization Policy Template


Anonymization is the method of reducing direct and indirect personal identifiers that could expose a person's identity.

Names, addresses, postcodes, phone numbers, photographs, images, and other distinctive personal features can all be used to identify a person.


Its production of specific data in a manner that prevents the data from becoming associated with a particular data subject without the use of additional information provided that such additional information is stored separately and is subject to technical and organizational measures to ensure non-attribution to an identified or recognizable individual.

Advantages of Anonymization and Pseudonymization

According to the GDPR, applying techniques, including pseudonymization to private data, can minimize the risk to data controllers and allow data controllers and processors to fulfill their data protection duties.

Anonymization also aids in ensuring data transfers, which are becoming more and more necessary in the digital economy. Additionally, anonymization lessens the likelihood that the data will be utilized in undesirable ways. 

Lastly, the security provided by the anonymization and pseudonymization methods helps an organization's information privacy rules be future-proofed and lower the cost of any fines and enforcement actions.

Is Data Personal?

GDPR, "private details" refers to anything of a named or recognizable natural person ('info').

Primarily a single identifiable single individual can be one who can be ascertained, directly or indirectly, by comparison to a designation such as a name, an identification number, location information, an online identifier, or to one or more factors specific to that natural person's physical, physiological, genetic, intellectual, financial, ethnic, or self-identification.

Data Anonymization In GDPR

If the anonymization is adequately carried out, the data will no longer be connected to a named or identifiable natural person and won't be regarded as personal information. Moreover, since the GDPR does not cover anonymous data is not covered by the GDPR, you have more freedom to utilize it.

There are two critical ways that the anonymization process can improve your company's compliance with data protection laws:

As part of the "information minimizing" plan, where data can be utilized and categorized without the risk of affecting the data subjects, or as part of the "privacy by design" strategic effort, which aims to increase the privacy of the processed data.

Techniques For Anonymization

Information is anonymized using various techniques, depending on the level of risk and the intended purpose of the data.

Techniques For Anonymization and  Pseudonymization

1. Scrambling: Techniques for scrambling involve combining or jumbling letters.

2. Masking: The real-time use of masking patterns for sensitive data improves customer data security is enhanced by the real-time use of masking designs for sensitive data.

3. Generalization: The generalization process is a process that removes some data to make it less recognized using a generalization process.

4. Swapping: Data are re-arranged using the swapping technique, so they don't resemble the primary data. For example, some data is removed to make it less recognized.

Techniques For Pseudonymization

Pseudonymization involves de-identification and replacing genuine identifiers with fraudulent ones.  Personal information is concealed so that it cannot be connected to a particular individual; without extra information, it cannot be related to a specific individual.

1. Encryption: When data is encrypted, it is converted into a format that cannot be read without decryption. Using this algorithm, a file can only be seen by someone using this algorithm; aa ale can only be seen by someone who also holds the decryption key.

2. Tokenization: By replacing non-sensitive data with sensitive data, tokens can protect sensitive data. Sensitive data is replaced with a unique token value during the innovative contracts process, which allows users to retrieve the original data.

3. Blurring: By estimating data values, a data-stretching approach leaves the original data outdated. This is the most often used method of preventing identification.

The possibility of revealing personal information can be effectively decreased by taking the below steps.

  • Determine and Sort Variables: Data must be anonymized to establish and categorize direct and indirect identifying characteristics, especially in papers where the facts and information are unstructured and classify natural and indirect identifying characteristics.
  • Therefore, clinical data must be anonymized, especially in forms where the facts and information are presented in an unstructured format.
  • Calculate the Risk of Re-Identification: The audit risk in the data and the risk associated with the release context interact to establish the overall risk of re-identification involved with the disclosure of clinical information. A solid quantitative risk assessment strategy (perhaps combined with a qualitative risk assessment process) is essential when determining the threat of re-identification, even if it would be too complicated to go into detail here. Once the data risk has been assessed, this risk assessment serves as a rationale for any potential data transformations.
  • Data Anonymization: The process used to anonymize clinical data can significantly impact valuable data. Therefore, it is recommended to avoid anonymizing variables that do not enhance the danger of re-identification and to use techniques that have the most negligible negative influence on the usefulness of the data.

Final Thoughts

In conclusion, creating an anonymization and pseudonymization policy is vital for protecting personal data and preserving individual privacy.

To ensure proper handling of personal information, organisations should give priority to developing clear policies and procedures for anonymization and pseudonymization.