Secure and Private AI

In this lesson, you'll learn about the basics of differential privacy, a method for measuring how operations impact the privacy of data.

In this lesson, you'll implement differential privacy in Python.

Learn how to apply differential privacy to arbitrary algorithms by adding noise to the outputs.

Learn how we can apply differential privacy to deep neural networks.

Learn about federated learning, a method for preserving data privacy by training models where the data lives.

Secure models trained using federated learning with multi-party computation.

Learn how to perform encrypted computation. Build an encrypted database, and generate an encrypted prediction with an encrypted neural network on an encrypted dataset.