Pseudoanonymization

Pseudoanonymization, or pseudonymization, is a data processing approach in which identifying information is replaced with artificial identifiers or is kept in a separate, secure file that links the identifier to the original identity. The aim is to reduce direct identifiability while preserving the usefulness of the data for analysis, testing, or processing tasks. Under many privacy frameworks, including the European Union's GDPR, pseudonymized data remains personal data because re-identification is possible if the linking information is accessed.

Key features include the separation and protection of the linkage between the pseudonyms and the original identities, and the use of technical controls to prevent unauthorized re-identification. The approach enables data to be shared or processed with reduced risk compared to raw personal data, though it does not eliminate privacy risk on its own.

Common techniques encompass pseudonymization (replacing identifiers with consistent tokens), tokenization (using a tokenization system to generate tokens), encryption with keys stored separately, and data masking. These methods are often part of a broader data protection strategy, balancing data utility with privacy by design and by default.

Compared with anonymization, pseudonymization does not remove all identifiers; the data can be re-identified with access to the key or linkage file. This makes ongoing governance, access control, key management, and audit trails essential. Pseudonymization is widely used to enable analytics, research, and data sharing while aiming to limit exposure if data are breached or misused.