FHE is a powerful cryptographic tool that allows cloud service providers to perform computations directly on encrypted data without needing to decrypt first. With FHE, developers can build applications which ensure full privacy of users and are, at the same time, the complete equivalent of their insecure counterparts.
With Concrete ML, we aim to make it as simple as possible for users of machine learning frameworks to understand and use FHE. To that end, we provide APIs which are as close as possible to what data scientists are already using.
Concrete ML Makes Use Cases Easy
Concrete ML can be used with models that are already familiar to data scientists. Logistic regressions, for example, are a popular class of algorithm in Machine Learning. With Concrete ML, you can build a simple logistic regression using scikit-learn to show that they can be executed homomorphically.
Concrete Numpy underpins Concrete ML. It is a python package that contains the tools data scientists need to compile various numpy functions into their FHE equivalents.
Still in an early version, it allows for example to turn some torch programs into numpy, and then to use the main API stack to finally get an FHE program.
Titanic Competition with Privacy Preserving Machine Learning
A Privacy-Preserving Machine Learning (PPML) solution to the famous ML Titanic challenge using concrete-mlRead Article
Announcing Concrete ML v0.2
We are announcing the release of Concrete ML as a public alpha. The package is built on top of Concrete Numpy.Read Article