There has been an extraordinary amount of hype attributed to Open AI’s recent GPT-3 API launch —it is very well-deserved. GPT-3 is a rema
Research has shown that it is possible to launch an attack where a malicious user uses Generative adversarial network (GANs) to recreate sam
OpenMinedOpenMined is an open source organisation whose mission is to lower the barrier to entry to privacy preserving technology. Thank you
This post is part of our Privacy-Preserving Data Science, Explained series. In this post, we cover: A brief outline of basic concepts: an ex
“Nothing in life is to be feared, it is only to be understood. Now is the time to understand more, so that we may fear less.” — Marie Curie
The newly created Events Team at OpenMined will focus on online events - everything from open meetups to presentations and webinars. As we h
This post is part of our Privacy-Preserving Data Science, Explained series.
On July 16, the ECJ invalidated the EU-US Privacy Shield, one of the key mechanisms for lawfully transferring data between the EU and the US. Federated Learning and other Privacy Preserving techniques would help solve some of the new challenges organizations face.
This post is part of our Privacy-Preserving Data Science, Explained series. Differential privacy has been established as the gold standard
Interview with Kyoko Eng, OpenMined's Featured Contributor for July 2020!
This post is part of our Privacy-Preserving Data Science, Explained series. In the era of XaaS(Anything as a Service), many companies provi
When we announced our first Bootcamp focused on Privacy-Preserving Machine Learning (PPML), we didn't expect the overwhelming interest we r
CrypTen integrated in PySyft: a fast SMPC backend for secure computation between servers.
As part of the PyTorch/OpenMined grants we announced last December, the Web & Mobile team has been hard at work on developing 4 new libraries for model-centric federated learning.
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!
