Do you want to help unlock advances in every scientific field by increasing humanity’s access to data by 1000x in a safe and secure way?Open
PETs have a vast number of real-world use cases. Researchers, businesses, regulators, governments, media organisations, startups and consum
Part 3: What is the impact of GDPR on the generation and sharing of synthetic datasets? The European General Data Protection Regulation (GD
Structured Transparency [1] is a framework for creating ideal information flows that guarantees that certain data only flows to the right de
Interview with Fiza HusainGithub: @fiza11 Where are you based? I'm currently based in New Delhi, India. What do you do (i.e. studying, work
Interview with Phil CullitonGithub: @pculliton Where are you based? I'm in Buffalo, NY. That's about six hours west of the New York City
Part 2: Where does synthetic data stand when it comes to the laws that protect user privacy? In a previous blog, we saw that synthetic data
Presently, the insurance and financial services sector face the classical data conundrum – there are troves of data available, yet not enou
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.
Interview with Ishan MishraGithub: @IshanMi Where are you based? I’m currently based in Waterloo, Canada. 🇨🇦 What do you do (i.e. stud
This blog post is a summary of the lessons from the Private AI series, Course-1: Our Privacy Opportunity. In this post we will talk about th
Part 1: Laws protect user information and regulate PET including synthetic datasets. But what exact disclosures plague synthetic data? The u
The rise of Privacy Enhancing Technologies (PETs) has witnessed tremendous growth in recent years. A recent research publication from MIT [1
Interview with Rasswanth S.Github: @rasswanth-s Where are you based? Tamil Nadu, India What do you do (i.e., studying, working, etc.)? I am
Today, we are proud to announce the Beta version of a framework for differential privacy in Python we call PipelineDP, which OpenMined built
A big part of OpenMined’s mission is to make privacy preserving machine learning (PPML) accessible. Differential privacy is one of the most
Summary: As algorithms increasingly influence critical decisions in society, there is a growing call for third-party and public audits. Howe
A gentle introduction to privacy budgets and accounting The word budget is defined as “an estimate of income and expenditure for a set peri
Interview with Ishan Mistry Github: @mistryishan25 Where are you based? "Gujarat, India " What do you do (i.e. studying, working, etc.)? "
Interview with Gonzalo Munilla Garrido, OpenMined's Featured Contributor for November 2021!
Synthetic data and validation of its expected behavior The research focused on the generation of synthetic data has gained pace like never
This is the summary of the paper 'Defending Medical Image Diagnostics against Privacy Attacks using Generative Methods' that was presented a
Interview with Daniel Huynh, OpenMined's Featured Contributor for December 2021!
Interview with Nabanita Dash, OpenMined's Featured Contributor for October 2021!
