Pseudonymization

Pseudonymization replaces identifying values, a name, an email, a customer ID, with an artificial identifier or token, while keeping the mapping between the token and the real identity stored separately, under restricted access. Unlike a masked value, a pseudonym is deliberately reversible by someone holding the mapping, which is what distinguishes it from anonymization.

Under General Data Protection Regulation, pseudonymized data is still considered personal data, because re-identification remains possible in principle, but the regulation explicitly recognizes pseudonymization as a risk-reducing safeguard that can justify broader processing than would otherwise be allowed. This is a narrower, more specific technique than data masking, which more often produces realistic but non-reversible substitutes for uses like software testing.

Pseudonymization matters in analytics because it lets teams work with behavioral or transactional data, joining events by a consistent pseudonymous ID, without most analysts ever touching real personally identifiable information. It underpins techniques like identity resolution performed on tokenized IDs rather than raw personal data. The common misreading is treating pseudonymized data as equivalent to anonymous data for regulatory purposes; because it can be re-identified via the retained mapping, it generally remains subject to privacy obligations, and specific requirements depend on the applicable law.