Explore comprehensive strategies and patterns for ensuring data privacy and compliance in AI systems, including techniques like anonymization, differential privacy, and federated learning, alongside practical implementations and ethical considerations.
Explore the techniques and principles of privacy-preserving machine learning, including federated learning, differential privacy, and homomorphic encryption, to protect individual data privacy in AI models.