This category focuses on machine learning methods with built-in privacy protections to safeguard sensitive data during training, inference, and deployment. It includes approaches such as differential privacy, secure multi-party computation, homomorphic encryption, and privacy-preserving federated learning. Research addresses the trade-off between model performance and data confidentiality.