08/09/2026
Episode 3 of our data anonymizationtechniquesseries: Pseudonymization.
Where masking hides values outright, pseudonymization swaps direct identifiers for consistent stand-in values. Records still join correctly across tables; the people behind them stay unnamed. This is important for when a dataset has to stay analytically useful for model training and evaluation.
The caveat practitioners should hold onto, though: pseudonymized data is still personal data under the NDPA and comparable regimes because the key that reverses the mapping exists somewhere.
It is risk reduction, not anonymization per se. The mapping table deserves the same protection as the source records.
Generalization and tokenization are coming up next in the series.