{"version":"1.0","type":"rich","provider_name":"gaks.ai AI Glossary","provider_url":"https://gaks.ai/glossary","title":"Privacy-Preserving Machine Learning — AI Glossary","author_name":"Glenn Katrud Solheim","author_url":"https://gaks.ai","width":600,"height":200,"html":"

AI Glossary — gaks.ai

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.

Source: gaks.ai/glossary/privacy-preserving-machine-learning →
"}