pseudonymization

**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.