Privacy-Preserving Machine Learning (PPML)
51 articles

Content on techniques that allow machine learning on private data without compromising security.

  • April 30, 2020

Privacy-Preserving AI Summary: MIT Deep Learning Series

  • April 15, 2020

PySyft, PyTorch and Intel SGX: Secure Aggregation on Trusted Execution Environments

  • April 13, 2020

PyGrid: A Peer-to-Peer Platform for Private Data Science and Federated Learning

  • April 11, 2020

Inference privacy: what is it, and why do we care?

  • March 19, 2020

Using Privacy and Federated Learning in Recommendations – Part 1

  • March 19, 2020

Autonomous Driving’s Seat Belt Moment

  • March 10, 2020

Predictive Maintenance of Turbofan Engines using Federated Learning with PySyft and PyGrid

  • February 7, 2020

Introduction to Federated Learning and Privacy Preservation using PySyft and PyTorch

  • February 3, 2020

Split Neural Networks on PySyft and PyTorch

  • January 21, 2020

Meet OpenMined’s new PyTorch-OpenMined Fellows

  • December 6, 2019

Announcing the OpenMined-PyTorch Federated Learning Fellowships

  • October 24, 2019

Introducing PySyft TensorFlow

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