Pseudonymization is a de-identification technique where identifiers are identified and replaced with realistic-looking fake values (surrogates) rather than being masked or deleted — preserving the linguistic structure and temporal relationships of the text.

Masking vs Pseudonymization

• Masking: "Patient [NAME] went to [HOSPITAL] on [DATE]." (Breaks readability/parsing).
• Pseudonymization: "Patient Alice went to General Hospital on Jan 1." (Preserves syntax).

Consistency

• Consistent: If "John" is mapped to "Bob" once, it must be "Bob" throughout the document (and dataset) to preserve coreference.
• Shifted Dates: All dates shifted by random $N$ days to preserve intervals (Time between admission and surgery remains 2 days) while hiding actual date.

Why It Matters

• Model Training: LLMs train better on fluent text (Pseudonymized) than broken text (Masked).
• Readability: Easier for human researchers to read.

Pseudonymization is fake identities — replacing real patient data with a consistent, realistic alias universe.