fhe

FHEVM, a full-stack framework for integrating Fully Homomorphic Encryption (FHE) with blockchain applications

An FHE compiler for C++

Concrete: TFHE Compiler that converts python programs into FHE equivalent

Concrete ML: Privacy Preserving ML framework using Fully Homomorphic Encryption (FHE), built on top of Concrete, with bindings to traditional ML frameworks.

This is the development repository for the OpenFHE library. The current development version is 1.4.0 (released on August 18, 2025). The current stable version is 1.3.1 (released on July 11, 2025).

A Solidity library for interacting with fhevm.

Cingulata (pronounced "tchingulata") is a compiler toolchain and RTE for running C++ programs over encrypted data by means of fully homomorphic encryption techniques.

A curated list of amazing Fully Homomorphic Encryption (FHE) resources created by the team at Zama.

Mind Network Rust SDK DeepSeek

List of Codes by Mind Network

A Fully Homomorphic Encryption (FHE) library for bridging the gap between theory and practice with a focus on performance and accuracy.

Cypher, FHE-EVM Layer is the core of the DeAI ecosystem, designed to optimize blockchain for AI applications. It integrates Fully Homomorphic Encryption (FHE) with Ethereum Virtual Machine (EVM) compatibility, allowing encrypted AI computation without sacrificing performance.

Pure C++ Ver. of TFHE.

HEhub is a library for homomorphic encryption and its applications, and is part of the PrimiHub project.

HEonGPU is a high-performance library that optimizes Fully Homomorphic Encryption (FHE) on GPUs. Leveraging GPU parallelism, it reduces computational load through concurrent execution. Its multi-stream architecture minimizes data transfer overhead, making it ideal for large-scale encrypted computations with reduced latency.

Modern Cryptography Suite in C++17

Repository for the SoK paper on Fully Homomorphic Encryption (FHE) compilers.

Optimizing compiler for Fully Homomorphic Encryption (FHE)

We are a community of researchers and developers interested in advancing homomorphic encryption and other secure computation techniques.

Source code for the paper "Encrypted Image Classification with Low Memory Footprint using Fully Homomorphic Encryption"