We’re very excited to announce the next round of open-source software development grants in the OpenMined community, generously sponsored by the University of California San Francisco! These grants will focus on bringing data-centric federated learning with differential privacy budgeting to PyGrid.
When we talk about sensitive data and cloud computing, how can we guarantee the remote, secure and private execution of our applications? One possible solution to this problem is to run the application in a Trusted Execution Environment (TEE).
Recommendation systems are everywhere in our everyday life online — they can be incredibly useful, time-saving, and aid in our discovery of things relevant to our interests. Privacy-preserving recommendation systems can use better signals to build better models.
We’re very excited to announce the next round of grants sponsored by the PyTorch team! This grant will focus on developing “worker libraries”, allowing PySyft code to be executed in other environments like a mobile phone or web browser.
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.