Train smarter AI models by learning to safely and securely use distributed private data with differential privacy, federated learning, and encrypted computation techniques.
- Welcome to Scholarship Challenge
Welcome note to the course and challenge.
- Deep Learning with PyTorch
Hands on tutorials and introdutory notes to PyTorch Library.
- Introducing Differential Privacy
Basics of Differential Privacy, a method for measuring how operations impact the privacy of data.
- Evaluating the Privacy of a Function
Implementing Differential Privacy in Python.
- Introducing Local and Global Differential Privacy
Applying Differential Privacy to arbitrary algorithms by adding noise to the outputs.
- Differential Privacy for Deep Learning
Differential Privacy to Deep Neural Networks
- Federated Learning
Methods for preserving data privacy by training models where the data lives.
- Secruring Federated Learning
Secure models trained with multi-party computation.
- Encrypted Deep Learning
Performing encryted computation. Building an encrypted database, and generate an encrypted prediction with an encryted neural networkon on an encryted database.
