Privacy-Preserving Machine Learning (PPML) refers to techniques, tools, and processes that allow machine learning models to be trained, evaluated, and deployed without exposing sensitive data.
Pseudonymization is a data protection technique that obscures or replaces sensitive data with non-identifiable substitutes, or pseudonyms, while retaining the ability to revert to the original data under specific conditions.
