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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Concrete ML: Privacy Preserving ML framework using Fully Homomorphic Encryption (FHE), built on top of Concrete, with bindings to traditional ML frameworks.
| Date | Stars |
|---|---|
| 2026-07-24 | 1442 |
| 2026-07-25 | 1442 |
| 2026-07-28 | 1442 |
| 2026-07-30 | 1442 |
| 2026-08-06 | 1442 |
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growth rate 0.00%/day
<p align="center"> <!-- product name logo --> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://github.com/zama-ai/concrete-ml/assets/157474013/5ed658d7-0abd-4444-9063-99d8b76c2602"> <source media="(prefers-color-scheme: light)" srcset="https://github.com/zama-ai/concrete-ml/assets/157474013/7c67e594-5e2c-483e-858f-ce473a36e37f"> <img width=600 alt="Zama Concrete ML"> </picture> </p> <hr> <p align="center"> <a href="https://docs.zama.ai/concrete-ml"> 📒 Documentation</a> | <a href="https://zama.ai/community"> 💛 Community support</a> | <a href="https://github.com/zama-ai/awesome-zama"> 📚 FHE resources by Zama</a> </p> <p align="center"> <a href="https://github.com/zama-ai/concrete-ml/releases"><img src="https://img.shields.io/github/v/release/zama-ai/concrete-ml?style=flat-square"></a> <a href="LICENSE"><img src="https://img.shields.io/badge/License-BSD--3--Clause--Clear-%23ffb243?style=flat-square"></a> <a href="https://github.com/zama-ai/bounty-program"><img src="https://img.shields.io/badge/Contribute-Zama%20Bounty%20Program-%23ffd208?style=flat-square"></a> <a href="https://slsa.dev"><img alt="SLSA 3" src="https://slsa.dev/images/gh-badge-level3.svg" /></a> </p> ## About ### What is Concrete ML **Concrete ML** is a Privacy-Preserving Machine Learning (PPML) open-source set of tools built on top of [Concrete](https://github.com/zama-ai/concrete) by [Zama](https://github.com/zama-ai). It simplifies the use of fully homomorphic encryption (FHE) for data scientists so that they can automatically turn machine learning models into their homomorphic equivalents, and use them without knowledge of cryptography. Concrete ML is designed with ease of use in mind. Data scientists can use models with APIs that are close to the frameworks they already know well, while additional options to those models allow them to run inference or training on encrypted data with FHE. The Concrete ML model classes are similar to those in scikit-learn and it is also possible to convert PyTorch models to FHE. <br></br> ### Main features - **Built-in models**: Ready-to-use FHE-friendly models with a user interface that is equivalent to their the scikit-learn and XGBoost counterparts - **Customs models**: Concrete ML supports models that can use quantization-aware training. These are developed by the user using PyTorch or keras/tensorflow and are imported into Concrete ML through ONNX *Learn more about Concrete ML features in the [documentation](https://docs.zama.ai/concrete-ml).* <br></br> ### Use cases By leveraging FHE, Concrete ML can unlock a myriad of new use cases for machine learning, such as enabling secure and private data collaboration, protecting sensitive data while still allowing for analysis, and facilitating machine learning on data-sets that are subject to strict data privacy regulations, for instance - **Healthcare data analysis**: Improve patient care while maintaining privacy by allowing secure, confidential data sharing between healthcare providers. - **Financial services**: Facilitate secure financial data analysis for risk management and fraud detection, keeping client information encrypted and safe. - **Ad campaign tracking**: Create targeted advertising and campaign insights in a post-cookie era, ensuring user privacy through encrypted data analysis. - **Industries:** Enable predictive maintenance in the cloud while keeping sensitive data confidential, enhancing efficiency and data security. - **Biometrics:** Give the ability to create user authentication applications without having to reveal their identities. - **Government:** Enable governments to create digitized versions of their services without having to trust cloud providers. *See more use cases in the list of [demos](#demos).* <br></br> ## Table of Contents - **[Getting Started](#getting-started)** - [Installation](#installation) - [A simple example](#a-simple-example) - **[Resources](#resources)** - [Demos](#demos)
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Benoit Chevallier-Mames · France
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Melanciani · @zama-ai · France
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Luis Montero · @mistralai
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Alex Quint · @zama-ai
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:3c55bf3b3b4b2a37, topic:privacy