Always working towards our mission to make the world more privacy-preserving by lowering the barrier-to-entry to Privacy-Enhancing Technolo
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.
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.
In this post we’ll see how a private set intersection protocol can be built using 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.
In this blog, Sadegh Riazi explains Microsoft's Project HEAX. One of the main obstacles in leveraging Fully Homomorphic Encryption at large-
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.
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.
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
Interview with Ronnie Falcon, OpenMined's Featured Contributor for June 2020!
We’re very excited to announce the recipients of the latest round of open-source software development grants in the OpenMined community, gen
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.
The mission at OpenMined is to make the world more privacy-preserving by lowering the barrier-to-entry to privacy-preserving technologies th
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
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
Interview with Sourav Das, OpenMined's Featured Contributor for May 2020!
The compelling use cases for differential privacy are growing each day. Engineers at OpenMined have been busy building libraries to improve
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.
Want bureaus to score your credit without hoarding your data? Find out how FL can enable privacy-preserving, cross-border credit assessment.
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
As OpenMined continues to grow we are excited to announce the OpenMined Operations Team! The mission of the Operations Team is to empower ot
This post is part of our Privacy-Preserving Data Science, Explained Simply series.
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.
This post is part of our Privacy-Preserving Data Science, Explained series. In this article of the introductory series on Private ML, we wi
