Pseudonymisation

Pseudonymisation is a data processing technique that replaces identifying fields in a dataset with artificial identifiers, or pseudonyms, so individuals cannot be readily identified without additional information. It reduces privacy risk while preserving data usefulness for analysis. It differs from anonymisation in that the mapping between pseudonyms and real identities is kept separately and is typically recoverable, whereas anonymisation aims to make re-identification impossible.

Implementation methods include tokenization, deterministic or non-deterministic hashing with salt, encryption, and the use of stable identifiers. The pseudonymisation mapping is stored securely and access-controlled, with robust key management and audit trails. The process can be reversible if the mapping is available, which is common for pseudonymised data.

Benefits include safer data sharing, easier linkage across datasets for research and analytics, and stronger protection under data protection regimes. Limitations include that pseudonymised data is not inherently anonymous; re-identification remains possible if the mapping is compromised or if auxiliary data exists. It should be accompanied by governance measures such as data minimisation, access controls, retention policies, and incident response.

Legal context: under the GDPR, pseudonymisation is recognised as a security measure and a component of data protection by design and default. It permits processing of personal data under appropriate safeguards, without removing responsibilities to satisfy lawful bases and purpose limitations.

Common use cases are health research datasets, customer analytics, and operational logs where direct identifiers are removed but analytical value is preserved.