Privacy-Preserving Machine Learning

A collection of techniques that allow machine learning models to be trained or used while protecting the privacy of the underlying data. Methods include federated learning, differential privacy, secure multi-party computation, and homomorphic encryption, enabling useful AI capabilities without exposing sensitive individual data to the model trainer or service provider.