Privacy Engineering & Differential Privacy

This lesson explores privacy engineering as a foundational discipline for protecting individuals within AI systems. Learners will examine privacy-by-design principles, differential privacy, k-anonymity, l-diversity, and synthetic data techniques used to reduce privacy risks while maintaining analytical value. The lesson also addresses the balance between data utility and privacy protection, demonstrating how organizations integrate privacy safeguards into AI development and operations to support trust, compliance, and responsible innovation.