OpenMined Featured Contributor: July 2022

Interview with Osam Kyemenu-SarsahGithub: @factdroid Where are you based? I currently live in Accra, Ghana.What do you do (i.e. studying,

Case Study - Federated privacy preserving analytics for secure collaboration among Telco and partners to improve customer engagement

MotivationWhile consumers expect better customer experience and personalization from businesses, they are increasingly sensitive to privacy

Remote Data Science Part 3: Deploying a Single Domain Node

This post is a continuation of "Remote Data Science Part 2: Introduction to PySyft and PyGrid". Previous blog was about introduction to Py

Of Legal Tangles and Synthetic Datasets: HIPAA and Synthesis

Part 4: What is the impact of HIPAA on the generation and sharing of synthetic datasets? The Health Insurance Portability and Accountability

Remote Data Science Part 2: Introduction to PySyft and PyGrid

This post is a continuation of "Remote Data Science Part 1: Today’s privacy challenges in BigData". The previous blog talks about

OpenMined Featured Contributor: June 2022

Interview with Stephen GabrielGithub: @bitsofsteve Where are you based? I am currently based in Abuja, Nigeria 🇳🇬 What do you do (i.e.

Remote Data Science : Part 1 - Today’s privacy challenges in BigData

Privacy Challenges in BigData The Internet has radically increased the scale of information accessible to society and decreased friction. St

Work on AI's most exciting frontier, no PhD required!

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

Lessons from Our Privacy Opportunity Part 3 - Real World Use Cases of PETs

PETs have a vast number of real-world use cases. Researchers, businesses, regulators, governments, media organisations, startups and consum

Of Legal Tangles and Synthetic Datasets Part 3: GDPR and Synthesis

Part 3: What is the impact of GDPR on the generation and sharing of synthetic datasets? The European General Data Protection Regulation (GD

Lessons from Our Privacy Opportunity - Part 2 The Structured Transparency Revolution

Structured Transparency [1] is a framework for creating ideal information flows that guarantees that certain data only flows to the right de

OpenMined Featured Contributor: May 2022

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

OpenMined Featured Contributor: April 2022

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

Of Legal Tangles and Synthetic Datasets Part 2: Legality

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

Privacy Enhancing Technologies for the Insurance Domain

Presently, the insurance and financial services sector face the classical data conundrum – there are troves of data available, yet not enou

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.

OpenMined Featured Contributor: March 2022

Interview with  Ishan MishraGithub: @IshanMi Where are you based? I’m currently based in Waterloo, Canada. 🇨🇦 What do you do (i.e. stud

Lessons from Our Privacy Opportunity: Part 1 Information flows

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

Of Legal Tangles and Synthetic Datasets Part 1: Data disclosures

Part 1: Laws protect user information and regulate PET including synthetic datasets. But what exact disclosures plague synthetic data? The u

Classifying the Challenges of Privacy Enhancing Technologies (PETs) in IoT Data Markets.

The rise of Privacy Enhancing Technologies (PETs) has witnessed tremendous growth in recent years. A recent research publication from MIT [1

OpenMined Featured Contributor: February 2022

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

A gentle introduction for applying Differential Privacy with PipelineDP

Today, we are proud to announce the Beta version of a framework for differential privacy in Python we call PipelineDP, which OpenMined built

Announcing PipelineDP

A big part of OpenMined’s mission is to make privacy preserving machine learning (PPML) accessible. Differential privacy is one of the most

Announcing our partnership with Twitter to advance algorithmic transparency

Summary: As algorithms increasingly influence critical decisions in society, there is a growing call for third-party and public audits. Howe