This repository is a curated list of resources on Fully Homomorphic Encryption (FHE), compiled by Zama. It serves as a comprehensive guide for researchers, developers, and practitioners interested in privacy-preserving computation, offering access to libraries, white papers, demos, tutorials, and research publications.

How It Works

The project focuses on the practical application and advancement of FHE. It highlights Zama's core FHE technologies: TFHE-rs (a Rust implementation), Concrete (an FHE compiler), Concrete ML (for privacy-preserving machine learning), and fhEVM (for confidential smart contracts on the EVM). These tools enable computations on encrypted data without decryption, preserving privacy for sensitive information.

Quick Start & Requirements

This repository is a curated list and does not have a direct installation or execution command. The linked resources within the README provide specific setup instructions for Zama's FHE libraries and frameworks.

Highlighted Details

• Comprehensive collection of FHE resources, including over 100 research papers and numerous tutorials.
• Showcases Zama's suite of FHE tools: TFHE-rs, Concrete, Concrete ML, and fhEVM.
• Features active development and community engagement through a bounty program with significant prize pools.
• Includes practical demonstrations and use cases across machine learning, blockchain, and data privacy.

Maintenance & Community

Zama actively maintains and contributes to the FHE ecosystem. The project highlights a substantial bounty program, indicating strong community involvement and incentivization for contributions. Links to Zama's blog, product releases, and announcements are provided for staying updated.

Licensing & Compatibility

The repository itself is a list of links and does not specify a license. The underlying Zama projects (TFHE-rs, Concrete, Concrete ML, fhEVM) are typically open-source, but specific licensing details for each should be verified on their respective repositories.

Limitations & Caveats

As a curated list, this repository does not provide direct FHE functionality. Users must refer to the individual linked projects for implementation details, dependencies, and potential limitations of each FHE library or framework. The complexity of FHE itself can present a steep learning curve.