What is pseudonymisation?
Pseudonymisation is the processing of personal data so that it cannot be attributed to a specific person without using additional information. Direct identifiers may be replaced with tokens, codes or keyed values, while the linking information is stored separately and protected by technical and organisational controls.
Pseudonymised data remains personal data because re-identification is still possible. The technique reduces risk and unnecessary exposure but does not remove the organisation’s privacy obligations.
How does pseudonymisation work?
Techniques include tokenisation, coded identifiers and keyed hashing. The method should prevent ordinary users of the dataset from identifying people while preserving the functionality needed for analysis, research or operations.
The linking key or lookup table is critical. It should be stored separately, accessed only by authorised roles, encrypted where appropriate and monitored. If the same pseudonym is reused broadly, datasets may be linked in unexpected ways.
Why is pseudonymisation useful?
It limits the number of people and systems exposed to direct identifiers and can reduce the impact of accidental disclosure. It supports analytics, testing and research where identity is not required for day-to-day work but may need to be restored under controlled conditions.
Pseudonymisation can be an important safeguard in risk assessments, but it should be combined with minimisation, access controls, retention and security. Weak governance can make the protection largely cosmetic.
How is pseudonymisation different from anonymisation?
Anonymisation aims to prevent reasonable re-identification, while pseudonymisation deliberately preserves a controlled method of linking data back to a person. Anonymised data may fall outside some privacy laws; pseudonymised data generally does not.
The distinction depends on practical capability and context rather than the name of the technique. A dataset described as anonymous may still be pseudonymous when the organisation holds a key.
Frequently asked questions
Is hashing always pseudonymisation?
Not automatically. Unsalted or predictable hashes may be reversed through guessing. The method, data and key management determine effectiveness.
Can pseudonymised data be shared freely?
No. It remains personal data and requires lawful purpose, security, contracts and appropriate transfer controls.
Who should access the re-identification key?
Only authorised roles with a defined need, under separation of duties, logging and approval controls.
Does pseudonymisation reduce breach risk?
Yes, when the exposed dataset cannot be linked without separately protected information, although other harm and re-identification risks may remain.
Can pseudonymisation support AI development?
Yes. It can reduce direct identifier exposure, but training purpose, data quality, bias and legal requirements still need assessment.