Introduction to THFE and its Applications

A summary of the talk at the OpenMined Privacy Conference 2020 Key Note Speakers Mariya Georgieva :- Director of Security Innovation at Inph

Accelerating deployment of healthcare ai by 1000x

Center for Digital Health Innovation partnered with GE to develop the world’s first FDA cleared AI (Artificial Intelligence) on a medical de

ML Privacy Meter: Aiding Regulatory Compliance by quantifying the privacy risks of machine learning

Conference Talk Summary by Sasi Kurakonda on ML Privacy Meter: Aiding Regulatory Compliance by quantifying the privacy risks of machine learning

Superfast Differential Privacy

A summary of the talk by Pranav Subramani at the OpenMined Privacy Conference 2020.

The Impact of Privacy Regulation on Effective Deployment of Technology during the COVID-19 Pandemic.

There were high expectations that the use of health records, cell tower tracking, and travel history could have ultimately helped to plateau the COVID-19 curve.

Pragmatic Security for Collaborative Learning

Multiple data owners holding data samples work together to train a model and solve a machine learning problem collaboratively while preserving some healthy mutual distrust is said to be Collaborative learning.

COINSTAC: Decentralized, Differentially Private Application for Neuroimaging

Summary of Talk by Eric Verner, Associate Director of Innovation at Centre for Translation Research in Neuroimaging and Data Science.MOTIVAT

Private AI: Machine Learning on Encrypted Data

Protect privacy of your data by encrypting it. Outsource computations on the encrypted data, and decrypt at your end to view results.

Private Deep Learning of Medical Data for Hospitals using Federated Learning and Differential Privacy

Speakers: Kritika Prakash , Lucile Saulnier, Dmitrii Usynin, Zarreen Naowal Reza This talk provides an example of  private deep learning u

Building Differential Privacy at Scale

Observe how a differential privacy infrastructure from the ground up. You'll see use cases that bolster the fact that making the infrastructure “user-centric” is crucial. We also discuss why people are reluctant to use such infrastructure

PRIMAL: a framework for secure evaluation of neural networks

This is a summary of the talk by Daniel Escudero at the OpenMined Privacy Conference 2020. Daniel Escudero talked about PRIMAL, a framewor

Adaptive Federated Optimization

In non-federated settings, adaptive optimization methods have desirable convergence properties. Can federated versions of these adaptive optimizers, including Adagrad, Adam, and Yogi facilitate better convergence in the presence of heterogeneous data?

Advances and Open Problems in Federated Learning

What are some of the recent advances in Federated Learning? What challenges do the privacy principles guiding Federated Learning (FL) bring into the system?

Why Venture Capitalists are Interested in Privacy Investing Now

This is a conference talk summary from the OpenMined Privacy Conference 2020 Trends and predictions for privacy startups with:Morgan Mahloc

Duet Demo - How to do data science on data owned by a different organization

Update as of November 18, 2021: The version of PySyft mentioned in this post has been deprecated. Any implementations using this older versi

What's in the TensorFlow Federated (TFF) box?

TensorFlow Federated (TFF) is a new development framework for Federated Computations (FC). Here's a summary of TFF's design goals and capabilities.

Tempered Sigmoid Activations for Deep Learning with Differential Privacy

Read to find out how tempered sigmoid activations help overcome the problem of exploding gradients and yield better accuracy under differentially private model training.

PrivacyRaven: Comprehensive Privacy Testing for Deep Learning

Access to only the output labels is a seemingly restrictive setting. What is an adversary modeled by PrivacyRaven capable of, given this restrictive setting?

Conference Summary: End-to-end privacy-preserving deep learning on multi-institutional medical imaging data

Speaker: Dr.Georgios Kaisssis, MHBA Video Link: Motivation:AI in medical imaging

Conference Talk Summary: Privacy-Preserving Natural Language Processing by Fatemehsadat Mireshghallah

This blog post summarises Fatemeh's Talk on privacy preserving NLP, showing the threats and mitigations with vulnerabilities in the NLP pipeline.

Conference Talk Summary: Helen Nissenbaum - Privacy, Contextual Integrity, and Obfuscation

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

What it's like to join the OpenMined Community

OpenMined aims to lower the barrier to entry in privacy preserving AI by offering a substantial fee free learning opportunity that can poten

What is [Meaningful Privacy]*

Meaningful privacy and how it is applied in technology will be the focus of 60 privacy preserving leaders from around the globe during the

Privacy AI startups & the OpenMined open-source community

OpenMined has built vibrant relationships with numerous startups, who are leveraging privacy AI technologies as a core part of their offer