Secure and Private AI

Overview

Refine your AI skills by mastering Differential Privacy, Federated Learning, and Encrypted Computation. Develop privacy-first models that protect sensitive data while enabling secure, scalable, and responsible machine learning.

Syllabus

Introducing Differential Privacy

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

Evaluating the Privacy of a Function

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

Introducing Local and Global Differential Privacy

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

Differential Privacy for Deep Learning

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

Federated Learning

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

Securing Federated Learning

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

Encrypted Deep Learning

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