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
synthetic data 2 years ago

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
privacy tech 2 years ago

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.
PriCon2020 2 years ago

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
Featured Contributor 2 years ago

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
Private AI Series 2 years ago

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
synthetic data 2 years ago

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
Differential Privacy 2 years ago

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
Featured Contributor 2 years ago

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
Differential Privacy 2 years ago

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
Announcements 2 years ago

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
2 years ago

Of Privacy and Purse Strings

A gentle introduction to privacy budgets and accounting The word budget is defined as “an estimate of income and expenditure for a set peri
Differential Privacy 2 years ago

OpenMined Featured Contributor: January 2022

Interview with Ishan Mistry Github: @mistryishan25 Where are you based? "Gujarat, India " What do you do (i.e. studying, working, etc.)? "
Featured Contributor 2 years ago

OpenMined Featured Contributor: December 2021

Interview with Gonzalo Munilla Garrido, OpenMined's Featured Contributor for November 2021!
Featured Contributor 2 years ago

Is My Fake Data Real Enough?

Synthetic data and validation of its expected behavior The research focused on the generation of synthetic data has gained pace like never
Private AI Series 2 years ago

Can privacy of medical images be ensured through proxy data?

This is the summary of the paper 'Defending Medical Image Diagnostics against Privacy Attacks using Generative Methods' that was presented a
Medical Imaging 3 years ago

OpenMined Featured Contributor: November 2021

Interview with Daniel Huynh, OpenMined's Featured Contributor for December 2021!
Featured Contributor 3 years ago

OpenMined Featured Contributor: October 2021

Interview with Nabanita Dash, OpenMined's Featured Contributor for October 2021!
Featured Contributor 3 years ago

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
PriCon2020 3 years ago

Design a federated learning system in seven steps

What should you consider when building an enterprise federated learning system?Photo by Hunter Harritt on UnsplashIntroductionCompanies like
Federated Learning 3 years ago

Fully Homomorphic Encryption (FHE) Frameworks

Attendees of First Workshop of Homomorphic Encryption (HE) communityHomomorphic Encryption cryptosystem is a cryptosystem whose decryption i
Homomorphic Encryption 3 years ago

Understanding the role of Privacy Enhancing Technologies (PETs) in the IoT Data Market.

The data generated by the Internet of Things (IoT) is leading a positive transformation across industries, not only in traditional ones such
Privacy 3 years ago

OpenMined Featured Contributor: September 2021

Interview with Ayoub Benaissa, OpenMined's Featured Contributor for September 2021!
Featured Contributor 3 years ago

Making autonomous vehicles robust with active learning, federated learning & V2X communication

When we think of driving in general, there are good drivers and bad drivers. So, on a 2D spectrum, we would picture a cluster of data of those drivers and realise that the good drivers’ data is clustered around a particular coordinate(x,y) while the bad drivers’ data is all over the place.
Use Cases 3 years ago