Glossary
What is pseudonymization?
Pseudonymization replaces the details that identify a person, such as a name or ID number, with stand-ins like [PERSON_001], and keeps the link between stand-in and real value in a separate, protected key. Whoever holds the key can reverse it; whoever does not sees consistent placeholders. Because it is reversible, regulators such as the EDPB and the UK ICO treat pseudonymized data as still personal data.
Last reviewed · 3 sources
The formal definition
NIST SP 800-188 defines pseudonymization as a "particular type of [de-identification] that both removes the association with a data subject and adds an association between a particular set of characteristics relating to the data subject and one or more pseudonyms."
The European Data Protection Board is explicit about the result: "Pseudonymised data, which could be attributed to a natural person by the use of additional information, is to be considered information on an identifiable natural person."
Example
"Maria Chen, SIN 046 454 286, applied on 3 March" becomes "[PERSON_001], SIN [SIN_001], applied on 3 March". Every later mention of Maria Chen becomes [PERSON_001] too, so a reader, or an AI, can still follow who did what. The key that maps [PERSON_001] back to Maria Chen is stored separately.
What it is not
- Not anonymization: anonymized data must not be reversible by anyone
- Not encryption of the whole document: the facts stay readable
- Not hashing: NIST notes that a repeatable transformation compromises identities if the method leaks; stand-ins that carry no information about the values, kept in a protected lookup table, avoid that
Where PiBye fits
How PiBye handles this
PiBye pseudonymizes documents on your Mac: consistent tokens for names, ID numbers and addresses, a key that never leaves the Mac, and a restore step that puts the real details back into the AI's returned document.
1.0.1 · macOS 14.8.5 or later · Apple Silicon · 1.1 GB
Frequently asked questions
Is pseudonymized data personal information?
Yes, for anyone who can reverse it, and the EDPB says it remains information on an identifiable person even when the key is held by someone else.
Why pseudonymize instead of deleting names?
Because the work has to come back. Consistent stand-ins let the AI reason about who did what, and the key lets you restore the names into the final document.
Sources
- NIST SP 800-188: De-Identifying Government Datasets, National Institute of Standards and Technology, September 2023. Checked 25 September 2026.
- Guidelines 01/2025 on Pseudonymisation (version for public consultation), European Data Protection Board, January 16, 2025. Checked 25 September 2026.
- How do we ensure anonymisation is effective?, Information Commissioner's Office (UK). Checked 25 September 2026.