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 grants will focus on bringing data-centric 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. Update as of November 18, 2021: The version of PySyft mentioned
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?
We are incredibly pleased to announce the OpenMined Learning Team. With the rising usage of Artificial Intelligence techniques to solve a wi
Madhava Jay, iOS ecosystem lead for OpenMined's Differential Privacy team recently released the alpha verision of SwiftDP. Here he shares hi
In this post, we introduce Syft, an open-source framework that integrates with PyTorch as well as TensorFlow, and show how to use it from R. In an example use case, we obtain private predictions from an R Keras model.
Update as of November 18, 2021: The version of PySyft mentioned in this post has been deprecated. Any implementations using this older versi
Our efforts in PSIWe recently published an article shining light on private set intersection (PSI) and its use in the COVID-19 crisis. We ar
In this blog post, we will cover a few use cases of differential privacy (DP) ranging from biomedical dataset analysis to geolocation.
This post is part of our Privacy-Preserving Data Science, Explained series. Private set intersection (PSI) is a powerful cryptographic techn
اَپهای بسیاری را ویژهٔ همه‌گیری کوویدِ ۱۹ همه‌جا دارند می‌سازند. می‌خواهند کمک کنند از تهدیدهای پیشِ روی جوامع چه در اقتصاد چه در سلامت کاسته شود. حریم خصوصیِ داده‌ها برای این اپ‌ها سرنوشت‌ساز است. نه‌تنها حریم خصوصی از حقوق بشر است، بلکه در جلب اعتماد و در پی‌اش اقبال به این اپ‌ها ضروری است.
Neural networks are not infallible. In this post we use a model inversion attack to discover data which was input to a target model.
