PrivacyRaven: Comprehensive Privacy Testing for Deep Learning

This is a summary of Suha S. Hussain’s talk in OpenMined Privacy Conference 2020 on PrivacyRaven - a comprehensive testing framework for sim

Federated Learning on Vessel Segmentation

It is a challenging task to acquire medical data for the deep learning models to train on. This blog gives a demo of how we can use Federated Learning to train our model on additional data without compromising the privacy of that data.

Private Deep Learning of Medical Data for Hospitals using Federated Learning and Differential privacy

Featuring Dmitrii Usynin - Speaker at #PriCon2020 - Sept 26 & 27 With the upcoming OpenMined Private Conference 2020 around the corner

Maintaining Privacy in Medical Data with Differential Privacy

How can you make use of these datasets without accessing them directly? How can you assure these hospitals that their patients’ data will be protected? Is it even a possibility?

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

When you ask your home voice assistant to check the weather, it is listening to and saving not only your voice but also everything else tha

Privacy-Preserving AI in Medical Imaging: Federated Learning, Differential Privacy, and Encrypted Computation

In medical imaging, necessary privacy concerns limit us from fully maximizing the benefits of AI in our research. These modern privacy techniques could allow us to train our models on encrypted data from multiple institutions, hospitals, and clinics without sharing the patient data.