pseudonimization

Pseudonimization, also spelled pseudonymization or pseudonymisation, is a data processing technique in which direct identifiers are replaced with pseudonyms or codes. The goal is to reduce the link between the data and the individual while preserving enough information to analyze the data or link records within the same dataset or across related datasets using a separate mapping.

In privacy and data protection contexts, pseudonimization is often described as a technical measure that lowers identifiability. It does not remove personal data from a record, and reidentification may still be possible if the mapping between pseudonyms and real identifiers or other identifying data is available. Consequently, the privacy protection it provides depends on secure handling, separation of keys or mappings, and strong access controls.

Common methods include tokenization (replacing identifiers with tokens), code substitution, encryption with a separate decryption key, and field-level masking. The process typically involves storing the mapping securely in a separate location or system, with restricted access, audit trails, and governance to prevent unauthorized reidentification.

Applications of pseudonymization include enabling data analytics, research, or data sharing while reducing exposure of directly identifying information. It is frequently used in healthcare, finance, and large-scale data projects where reidentification is controlled and minimized. However, it should be understood as a reversible technique whose effectiveness hinges on the security of the mapping and related processes, rather than as a standalone guarantee of anonymity.