Advanced Educational Data Anonymization Framework

Create a sophisticated Python system for anonymizing educational datasets while preserving statistical integrity and research value. Implement advanced privacy-preserving techniques including differential privacy, k-anonymity, and synthetic data generation. Develop a modular framework that can handle multiple data types and provide configurable anonymization strategies with detailed privacy impact assessments.