Comprehensive Privacy-Preserving Machine Learning Pipeline

Create an advanced privacy-preserving machine learning framework supporting multiple anonymization and secure computation techniques. Implement differential privacy mechanisms, federated learning approaches, and homomorphic encryption methods. Develop a modular system that can apply privacy constraints while maintaining model performance across various machine learning algorithms. Include comprehensive privacy budget tracking, utility assessment, and automated compliance reporting.