PySyft + Opacus: Federated Learning with Differential Privacy

We use Opacus from PyTorch and PySyft from OpenMined to combine Federated Learning with Differential Privacy.

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

Build PATE Differential Privacy in Pytorch

Summary: In this blog we’re going to discuss PATE - "Private Aggregation of Teacher Ensembles".  PATE is a private machine learning techniq

Differentially Private Deep Learning in 20 lines of code: How to use the PyTorch Opacus Library

I learn best from toy code I can play with. This tutorial teaches Differentially Private Deep Learning using a recently released library called Opacus.

What is Differential Privacy by Shuffling?

This post is part of our Privacy-Preserving Data Science, Explained series. Differential privacy has been established as the gold standard

Dev Diaries- Wrapping Differential Privacy for Python

The compelling use cases for differential privacy are growing each day. Engineers at OpenMined have been busy building libraries to improve

Privacy-Preserving Data Science, Explained

In this blog series, we’ll explain common topics in privacy-preserving data science, from a single sentence to code examples. We hope these posts serve as a useful resource for you to figure out the best techniques for your organization.

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?

Dev Diaries- Bringing Google's differential privacy to iOS - the struggle, the pain and the glory!

Madhava Jay, iOS ecosystem lead for OpenMined's Differential Privacy team recently released the alpha verision of SwiftDP. Here he shares hi

Use Cases of Differential Privacy

In this blog post, we will cover a few use cases of differential privacy (DP) ranging from biomedical dataset analysis to geolocation.

Roadmap to Differential Privacy for All

Team DP has been tasked with delivering differential privacy as a simple to use package for app developers, data engineers and machine learn

OpenMined is wrapping Google's differential privacy into your app (and we need you!)

Building an easy to use wrapper around a robust cryptography library for use in mobile apps and browsers. COVID-19 is not the first pandemic