Announcing The First OpenMined Bootcamp

Always working towards our mission to make the world more privacy-preserving by lowering the barrier-to-entry to Privacy-Enhancing Technolo

Private set intersection with Diffie-Hellman

In a previous post we looked at the Diffie-Hellman key exchange protocol. In this post, we’ll see how to use that as a basis to construct a private set intersection protocol.

Diffie-Hellman key exchange

This post introduces the Diffie-Hellman key exchange protocol. We can build a private set intersection protocol on top of the Diffie-Hellman key exchange protocol, as we’ll see next time.

Private set intersection with the Paillier cryptosystem

In this post we’ll see how a private set intersection protocol can be built using the Paillier cryptosystem.

The Paillier cryptosystem

This post introduces the Paillier cryptosystem, which is a partial homomorphic encryption scheme. In a subsequent post we’ll see how this can be used as the basis for a private set intersection protocol.

From Fully Homomorphic Encryption to Silicon - What is Microsoft's HEAX?

In this blog, Sadegh Riazi explains Microsoft's Project HEAX. One of the main obstacles in leveraging Fully Homomorphic Encryption at large-

Encrypted Training on Medical Text Data using SyferText and PyTorch

Healthcare data is highly regulated and should be, for most intents and purposes, private. Here, we demonstrate encrypted training on medical text data using SyferText and PyTorch.

Encrypted Gaussian Naive Bayes from scratch

Naive Bayes methods are probabilistic models which are useful when dimensionality of the dataset is high. Gaussian Naive Bayes can be used when the dataset with continuous values. Here, we demonstrate encrypted gaussian naive bayes from scratch.

Announcing the OpenMined Privacy Conference 2020

To be livestreamed on Sat 26th/Sun 27th September 2020We are very excited to announce OpenMined’s very first conference! Over two days in la

OpenMined Featured Contributor: June 2020

Interview with Ronnie Falcon, OpenMined's Featured Contributor for June 2020!

Meet OpenMined's new UCSF-OpenMined Fellows

We’re very excited to announce the recipients of the latest round of open-source software development grants in the OpenMined community, gen

Announcing the OpenMined-UCSF Dynamic Federated Learning Fellowship

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 fellowships will focus on bringing dynamic federated learning with differential privacy budgeting to PyGrid.

OpenMined + Genesis Cloud Partnership For Cloud Infrastructure and Applications

The mission at OpenMined is to make the world more privacy-preserving by lowering the barrier-to-entry to privacy-preserving technologies th

Looking for a Kotlin/Java/Android developer to join our PSI team!

Our efforts in PSIWe recently published an article shining light on private set intersection (PSI) and  its use in the COVID-19 crisis. We a

PySyft Tutorial in Bengali

Welcome! OpenMined is an open-source community whose goal is to make the world more privacy-preserving by lowering the barrier-to-entry to p

OpenMined Featured Contributor: May 2020

Interview with Sourav Das, OpenMined's Featured Contributor for May 2020!

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

Sentiment Analysis on Multiple Datasets With SyferText - Demo

How can you do pre-processing if you are not allowed to have access to plaintext data? SyferText can help you! With SyferText, you can define pre-processing components to perform pre-processing remotely, blindly and in a completely secure fashion.

Federated Learning for Credit Scoring

Want bureaus to score your credit without hoarding your data? Find out how FL can enable privacy-preserving, cross-border credit assessment.

A Federated Learning Approach for Pill Identification

Alright, so you’ve built an MNIST classifier using Federated Learning. Now it’s time to build something a little more cooler. Let’s build a

Announcing the OpenMined Operations Team

As OpenMined continues to grow we are excited to announce the OpenMined Operations Team! The mission of the Operations Team is to empower ot

What is Secure Multi-Party Computation?

This post is part of our Privacy-Preserving Data Science, Explained Simply series.

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.

What is Federated Learning?

This post is part of our Privacy-Preserving Data Science, Explained series. In this article of the introductory series on Private ML, we wi