
Private AI: Practical applications of ML via cloud-based or machine-learning-as-a-service platforms pose a range of security and privacy challenges. There are a number of technical approaches being studied including: homomorphic encryption, secure multi-party computation, trusted execution environments, on-device computation, and differential privacy. To provide an immersive understanding of how some of these technologies are applied, we will use the CrypTen project which provides a community based research platform to take the field of Private AI forward.

We’d like to thank the entire PyTorch team and the community for all their contributions to this work.
Cheers!
Team PyTorch