Answer questions using data you cannot see.
We seek to make privacy-preserving AI easy by extending the major Deep Learning frameworks (PyTorch, Tensorflow, and Keras) with techniques for privacy such as: Federated Learning, Homomorphic Encryption, Secure Multi-party Computation, and Differential Privacy.
Let's make the world more privacy preserving!
OpenMined 2020 Projects
Implement Auto-Scaling of PyGrid servers on Google CloudThe audience of PySyft largely consists of people who would like to train their model on private data that reside on other devices/locations. Right...
Implement Fan-Vercauteren Homomorphic Encryption Scheme in PySyftFV (Fan-Vercauteren) Homomorphic Encryption scheme is one of the leading approaches in homomorphic encryption. Homomorphic encryption is a form of...
Performing a security audit on privacy-preserving, distributed learning methodologies running on PyGrid with respect to GDPR requirement.In order to bridge the gap between industry and new technology, this project will supply governance models which are relevant to Privacy-Preserving...
Wrap Open-License Zero-Knowledge Proof LibraryZero-knowledge proofs have an important role to play in the future of verified machine learning prediction. However, no deep learning framework has...