Apply differential privacy techniques to add noise to the sensitive fields, ensuring individual data points cannot be traced back while preserving the overall data distribution.

Remove all sensitive data fields entirely, instructing the data science team to rely solely on non-sensitive data for model building, despite potential loss in model accuracy.

Tokenize each sensitive field by replacing real values with hashed dummy values, ensuring original data isn't exposed while still usable for model training.

Simplify the data by categorizing AGE into quantiles and reducing LATITUDE_LONGITUDE precision to a single decimal place, arguing that the remaining fields are already simplified as much as possible.

Encrypt the sensitive fields using a secure encryption algorithm before sharing with the data science team, requiring decryption keys for access during model training.