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

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.

A list of Companies, Startups, and Projects in the Privacy Space

The data protection market is estimated to reach $119 billion by 2022 — this projection highlights the pivotal role the field has in societ

Sentiment Analysis on Multiple Datasets With SyferText - Demo

How can you do pre-processing if you are not allowed to have access to plaintext data? SyferText can help you! With SyferText, you can define pre-processing components to perform pre-processing remotely, blindly and in a completely secure fashion.

Use Cases of Differential Privacy

In this blog post, we will cover a few use cases of differential privacy (DP) ranging from biomedical dataset analysis to geolocation.

A privacy-preserving way to find the intersection of two datasets

This post is part of our Privacy-Preserving Data Science, Explained series. Private set intersection (PSI) is a powerful cryptographic techn

Genetic data privacy in the dawn of big data forensics

“You would see hundreds and hundreds of unsolved crimes solved overnight,” Detective Michael Fields of the Orlando Police Department, from

Inference privacy: what is it, and why do we care?

When you ask your home voice assistant to check the weather, it is listening to and saving not only your voice but also everything else tha

Using Privacy and Federated Learning in Recommendations - Part 1

Recommendation systems are everywhere in our everyday life online — they can be incredibly useful, time-saving, and aid in our discovery of things relevant to our interests. Privacy-preserving recommendation systems can use better signals to build better models.

Predictive Maintenance of Turbofan Engines using Federated Learning with PySyft and PyGrid

Is it possible to benefit from the wonders of machine learning without having direct access to data? Today, machine learning can be used to

OpenMined + apheris AI Partnership for PyTorch Mobile Federated Learning

Today, we’re very excited to announce our Use Case partnership with apheris AI to deploy the very first open-source system for private federated learning on server, web, and mobile at scale.