Daniel Huynh - Mithril Security Blog

Jupyter Notebooks Are Not Made for Sensitive Data Science Collaboration

When collaborating remotely on sensitive data, their usually amazing interactivity and flexibility need safeguards, or whole datasets can be extracted in a few lines of code.

bastionlab

Introducing BastionLab - A simple privacy framework for data science collaboration

BastionLab is a simple privacy framework for data science collaboration. It lets data owners protect the privacy of their datasets and enforces that only privacy-friendly operations are allowed on the data and anonymized outputs are shown to the data scientist.

Our roadmap to build a simple privacy toolkit for data science collaboration

One year and a half later, Mithril Security’s roadmap has transformed significantly, but our initial goal stayed the same: democratizing privacy in data science.

blindai

Announcing BlindAI 0.5 with Cloud support

BlindAI 0.5 is now available with many new features such as Cloud support!

bastionai

Introducing BastionAI, an open-source privacy-friendly AI training framework in Rust

Discover BastionAI, a Rust project for Confidential deep learning training. BastionAI leverages Confidential Computing and Differential Privacy to make AI training between multiple parties more privacy-friendly

mithril-security

Mithril Security joins the Confidential Computing Consortium

Mithril Security joins the Confidential Computing Consortium to accelerate open-source privacy friendly AI

blindai

Presenting Mithril Cloud, the first Confidential AI as a Service offering

Discover how BlindAI Cloud enables you to deploy and query AI models with privacy, just from 2 lines of Python code. Try our solution with the deployment of a ResNet model.

large language models

Large Language Models and privacy. Part 1: how can privacy accelerate the adoption of big models

We will see why security and privacy might facilitate the adoption of Large Language Models, as those vast models push towards centralisation, given the complexity of deploying them at scale.