You developed a tree model based on an extensive feature set of user behavioral data. The model has been in production for 6 months. New regulations were just introduced that require anonymizing personally identifiable information (PII), which you have identified in your feature set using the Cloud Data Loss Prevention API. You want to update your model pipeline to adhere to the new regulations while minimizing a reduction in model performance. What should you do?

Hashing is an irreversible transformation that ensures anonymization and does not lead to an expected drop in model performance because you keep the same feature set while enforcing referential integrity. Removing features from the model does not keep referential integrity by maintaining the original relationship between records, and is likely to cause a drop in performance. Masking does not enforce referential integrity, and a drop in model performance may happen. Also, tuning the existing model is not recommended because the model training on the original dataset may have memorized sensitive information. Deterministic encryption is reversible, and anonymization requires irreversibility.