Secure and Private AI

What’s the earliest we can predict cancer survival rates, and what schools do the best job of educating children? You can only answer these questions with very rare access to private and personal data, but access to this personal data requires that you master methods for the principled protection of user privacy. While not all privacy use cases have been solved, the last few years have seen great strides in privacy-preserving technologies.

This free course will introduce you to three cutting-edge technologies for privacy-preserving AI: Federated Learning, Differential Privacy, and Encrypted Computation. You will learn how to use the newest privacy-preserving technologies, such as OpenMined's PySyft. PySyft extends Deep Learning tools—such as PyTorch—with the cryptographic and distributed technologies necessary to safely and securely train AI models on distributed private data.

We encourage you to enter the Secure and Private AI Scholarship Challenge from Facebook to both take the course and have a chance to win a scholarship for the Deep Learning or Computer Vision Nanodegree programs.

• Differential Privacy
• Learn the mathematical definition of privacy,Train AI models in PyTorch to learn public information from within private datasets
• Federated Learning
• Train on data that is highly distributed across multiple organizations and data centers using PyTorch and PySyft,Aggregate gradients using a "trusted aggregator"
• Encrypted Computation
• Do arithmetic on encrypted numbers,Use cryptography to share ownership over a number using Secret Sharing,Leverage Additive Secret Sharing for encrypted Federated Learning