Secure and Private AI is a comprehensive course on Udacity that covers various aspects of privacy and security in the field of artificial intelligence. The course begins by teaching the mathematical definition of privacy, enabling students to understand how to protect sensitive information. It then delves into training AI models in PyTorch, allowing learners to extract public information from private datasets. The course also explores federated learning, which involves training models on distributed data across multiple organizations and data centers using PyTorch and PySyft. Students will learn how to aggregate gradients using a trusted aggregator to ensure privacy and security. Additionally, the course covers encrypted computation, teaching students how to perform arithmetic operations on encrypted numbers. It also introduces the concept of Secret Sharing, a cryptographic technique that enables the secure sharing of ownership over a number. Finally, learners will leverage Additive Secret Sharing for encrypted Federated Learning, combining privacy and security in a federated learning setting.