Privacy-Preserving Machine Learning in Practice
Privacy-Preserving Machine Learning in Practice Privacy-preserving machine learning helps teams use data responsibly. You can build useful models without exposing individual details. The goal is to protect people while keeping value in analytics and products. Key methods are practical and often work together. Differential privacy adds controlled noise so results stay useful but protect each person. Federated learning trains models across many devices or sites and shares only updates, not raw data. Secure multiparty computation lets several parties compute a result without revealing their inputs. Homomorphic encryption is powerful but can be heavy for large tasks. Data minimization and synthetic data reduce exposure, while governance and audits keep things on track. ...