“Humans are not perfect. They can get distracted and sometimes miss out on subtle speech cues and warning signs. Unfortunately, there is no
Interview with Helena Barmer, OpenMined's Featured Contributor for January 2021!
Update as of November 18, 2021: The version of PySyft mentioned in this post has been deprecated. Any implementations using this older versi
TensorFlow Federated (TFF) is a new development framework for Federated Computations (FC). Here's a summary of TFF's design goals and capabilities.
The use of my private information has long bothered me. However, I have not done much about the right to be forgotten.
Since you clicked open the link to this post, may I be so bold as to assume that you, my dear reader, are as concerned about internet privac
Read to find out how tempered sigmoid activations help overcome the problem of exploding gradients and yield better accuracy under differentially private model training.
Interview with Laura Ayre, OpenMined's Featured Contributor for December 2020!
Access to only the output labels is a seemingly restrictive setting. What is an adversary modeled by PrivacyRaven capable of, given this restrictive setting?
Interview with Madhava Jay, OpenMined's Featured Contributor for November 2020!
Speaker: Dr.Georgios Kaisssis, MHBA Video Link: https://www.youtube.com/watch?v=F46lX5VIoas&t=21m50s Motivation:AI in medical imaging
Providing encryption to data is good, but to allow working on the encrypted data can be much more beneficial. Here, we explain the concept of Functional Encryption and give a brief comparison with Homomorphic Encryption.
This blog post summarises Fatemeh's Talk on privacy preserving NLP, showing the threats and mitigations with vulnerabilities in the NLP pipeline.
The Reviewed Paper: Preserving Differential Privacy in Convolutional Deep Belief Networks (💐Authors: Nhat Hai Phan, Xintao Wu, Dejing Dou)
Fifth part of the series CKKS explained where we see how to define rescaling
Across research institutions, personal devices, and private companies, humankind is gathering a huge amount of information about ourselves a
Dr. Helen Nissenbaum is one of the world’s most influential philosophers in the privacy space. She is the author of “Privacy in Context: Tec
New paradigms and skills are spread most effectively through education, so we’re building an entirely new learning platform starting with a series of courses on privacy-preserving machine learning.
The delayed results in the 2020 U.S. election has fueled accusations of fraud, which may yet spill into civil unrest. This blog looks at e-voting as a possible solution to the problems at the heart of this election.
Learn from data, without sacrificing privacy.
It is a challenging task to acquire medical data for the deep learning models to train on. This blog gives a demo of how we can use Federated Learning to train our model on additional data without compromising the privacy of that data.
Fourth part of the series CKKS explained where we see how to define ciphertext multiplication and relinearization
Interview with Chris Briggs, OpenMined's Featured Contributor for October 2020!
In this tutorial, you are going to learn how to setup PySyft, a privacy-preserving machine learning framework, on Windows 10.
