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https://github.com/zama-ai/concrete

Concrete: TFHE Compiler that converts python programs into FHE equivalent
https://github.com/zama-ai/concrete

compiler crypto cryptography encryption fhe fully-homomorphic-encryption gdpr homomorphic-encryption homomorphic-encryption-library llvm mlir privacy python security tfhe

Last synced: 2 days ago
JSON representation

Concrete: TFHE Compiler that converts python programs into FHE equivalent

Host: GitHub
URL: https://github.com/zama-ai/concrete
Owner: zama-ai
License: other
Created: 2020-10-01T08:32:30.000Z (over 4 years ago)
Default Branch: main
Last Pushed: 2024-10-29T14:47:46.000Z (4 months ago)
Last Synced: 2024-10-29T15:23:31.901Z (4 months ago)
Topics: compiler, crypto, cryptography, encryption, fhe, fully-homomorphic-encryption, gdpr, homomorphic-encryption, homomorphic-encryption-library, llvm, mlir, privacy, python, security, tfhe
Language: C++
Homepage:
Size: 42.6 MB
Stars: 1,232
Watchers: 24
Forks: 144
Open Issues: 23
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
- Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

awesome-zama - Concrete
awesome-cryptography-rust - Concrete - Enables computations over encrypted data. (Cryptography / Homomorphic Encryption)
awesome-privacy-engineering - Concrete - The concrete ecosystem is a set of crates (packages in the Rust language) that implements Zama's variant of TFHE, while most of the complexity of fully homomorphic encryption is hidden under high-level APIs. (Awesome Privacy Engineering [![Awesome](https://awesome.re/badge.svg)](https://awesome.re) / Homomorphic Encryption)
awesome-fhe - Concrete
awesome-he - Concrete - TFHE compiler for converting Python programs into FHE equivalents. (Toolkits)
awesome-rust-security-guide - concrete - rs](https://github.com/tfhe/tfhe-rs) implement various HE primitives and schemes. (6. Privacy-Preserving Technologies / 6.3 Homomorphic Encryption)
awesome-rust-security-guide - concrete - networks/paillier) implement various HE primitives and schemes. (6. Privacy-Preserving Technologies / 6.3 Homomorphic Encryption)

README

        


  

  

  







   📒 Documentation |  💛 Community support |  📚 FHE resources by Zama





  

  

  

  



## About

### What is Concrete

**Concrete** is an open-source FHE Compiler that simplifies the use of fully homomorphic encryption (FHE). Built on TFHE technology and leveraging LLVM, Concrete makes writing FHE programs accessible to developers without deep cryptography expertise.

#### Key Features

- 🚀 Simple Python API for FHE operations

- 🔒 Built-in security guarantees

- ⚡ GPU acceleration support

- 🛠 Automatic parameter selection

- 📊 Built-in performance analysis tools

- 🔄 Seamless integration with existing Python code

Fully Homomorphic Encryption (FHE) enables performing computations on encrypted data directly without the need to decrypt it first. FHE allows developers to build services that ensure privacy for all users. FHE is also an excellent solution against data breaches as everything is performed on encrypted data. Even if the server is compromised, no sensitive data will be leaked.

Concrete is a versatile library that can be used for a variety of purposes. For instance, [Concrete ML](https://github.com/zama-ai/concrete-ml) is built on top of Concrete to simplify Machine-Learning oriented use cases.




## Table of Contents

- **[Getting Started](#getting-started)**

   - [Installation](#installation)

   - [A simple example](#a-simple-example)

- **[Resources](#resources)**

   - [Concrete deep dive](#concrete-deep-dive)

   - [Tutorials](#tutorials)

   - [Documentation](#documentation)

- **[Working with Concrete](#working-with-concrete)**

   - [Citations](#citations)

   - [Contributing](#contributing)

   - [License](#license)

- **[Support](#support)**




## Getting Started

### System Requirements

- Python 3.8 - 3.12

- 8GB RAM minimum (16GB recommended)

- x86_64 or ARM64 processor

###  Installation

We provide multiple installation methods to suit your needs:

#### 1. PyPI Installation (Recommended)

For CPU-only version:

```shell

pip install -U pip wheel setuptools

pip install concrete-python

```

For GPU-accelerated version:

```shell

pip install -U pip wheel setuptools

pip install concrete-python --index-url https://pypi.zama.ai/gpu

```

#### 2. Docker Installation

```shell

# CPU version

docker pull zamafhe/concrete-python:v2.0.0

# GPU version

docker pull zamafhe/concrete-python:v2.0.0-gpu

```

#### Version Matrix

| Concrete Version | Python Version | CUDA Support |

|-----------------|----------------|--------------|

| 2.0.0           | 3.8 - 3.12     | ≥ 11.8      |

| 1.1.0           | 3.8 - 3.10     | ≥ 11.7      |

*Find more detailed installation instructions in [this part of the documentation](https://docs.zama.ai/concrete/getting-started/installing)*



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### A simple example

To compute on encrypted data, you first need to define the function you want to compute, then compile it into a Concrete Circuit, which you can use to perform homomorphic evaluation.

Here is the full example:

```python

from concrete import fhe

def add(x, y):

    return x + y

compiler = fhe.Compiler(add, {"x": "encrypted", "y": "encrypted"})

inputset = [(2, 3), (0, 0), (1, 6), (7, 7), (7, 1), (3, 2), (6, 1), (1, 7), (4, 5), (5, 4)]

print(f"Compilation...")

circuit = compiler.compile(inputset)

print(f"Key generation...")

circuit.keygen()

print(f"Homomorphic evaluation...")

encrypted_x, encrypted_y = circuit.encrypt(2, 6)

encrypted_result = circuit.run(encrypted_x, encrypted_y)

result = circuit.decrypt(encrypted_result)

assert result == add(2, 6)

```

*This example is explained in more detail [in this part of the documentation](https://docs.zama.ai/concrete/get-started/quick_start).*



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>[!Note]

>**Zama 5-Question Developer Survey**

>

>We want to hear from you! Take 1 minute to share your thoughts and helping us enhance our documentation and libraries. **[👉 Click here](https://www.zama.ai/developer-survey)** to participate. 

## Resources

### Concrete deep dive

- [Part I: Concrete, Zama's Fully Homomorphic Compiler](https://www.zama.ai/post/zama-concrete-fully-homomorphic-encryption-compiler)

- [Part II: The Architecture of Concrete, Zama's Fully Homomorphic Encryption Compiler Leveraging MLIR](https://www.zama.ai/post/the-architecture-of-concrete-zama-fully-homomorphic-encryption-compiler-leveraging-mlir)




### Tutorials

- [[Video tutorial] Dive into Concrete - Zama's Fully Homomorphic Encryption Compiler](https://www.zama.ai/post/video-tutorial-dive-into-concrete-zamas-fully-homomorphic-encryption-compiler)

- [[Video tutorial] How To Get Started With Concrete - Zama's Fully Homomorphic Encryption Compiler](https://www.zama.ai/post/how-to-started-with-concrete-zama-fully-homomorphic-encryption-compiler)

- [The Encrypted Game of Life in Python Using Concrete](https://www.zama.ai/post/the-encrypted-game-of-life-using-concrete-python)

- [Encrypted Key-value Database Using Homomorphic Encryption](https://www.zama.ai/post/encrypted-key-value-database-using-homomorphic-encryption)

*Explore more useful resources in [Concrete tutorials](https://docs.zama.ai/concrete/v/main-1/tutorials/see-all-tutorials) and [Awesome Zama repo](https://github.com/zama-ai/awesome-zama?tab=readme-ov-file#concrete). If you have built awesome projects using Concrete, please let us know and we will be happy to showcase them here!*

### Documentation

Full, comprehensive documentation is available at [https://docs.zama.ai/concrete](https://docs.zama.ai/concrete).



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## Working with Concrete

### Citations

To cite Concrete in academic papers, please use the following entry:

```text

@Misc{Concrete,

  title={{Concrete: TFHE Compiler that converts python programs into FHE equivalent}},

  author={Zama},

  year={2022},

  note={\url{https://github.com/zama-ai/concrete}},

}

```

### Contributing

There are two ways to contribute to Concrete. You can:

- [Open issues](https://github.com/zama-ai/concrete/issues/new/choose) to report bugs and typos, or to suggest new ideas

- Request to become an official contributor by emailing [email protected].

Becoming an approved contributor involves signing our Contributor License Agreement (CLA). Only approved contributors can send pull requests (PRs), so get in touch before you do!

Additionally, you can contribute to advancing the FHE space with Zama by participating in our [Bounty Program and Grant Programs](https://github.com/zama-ai/bounty-and-grant-program)!




### License

This software is distributed under the **BSD-3-Clause-Clear** license. Read [this](LICENSE.txt) for more details.

#### FAQ

**Is Zama’s technology free to use?**

>Zama’s libraries are free to use under the BSD 3-Clause Clear license only for development, research, prototyping, and experimentation purposes. However, for any commercial use of Zama's open source code, companies must purchase Zama’s commercial patent license.

>

>Everything we do is open source and we are very transparent on what it means for our users, you can read more about how we monetize our open source products at Zama in [this blog post](https://www.zama.ai/post/open-source).

**What do I need to do if I want to use Zama’s technology for commercial purposes?**

>To commercially use Zama’s technology you need to be granted Zama’s patent license. Please contact us at [email protected] for more information.

**Do you file IP on your technology?**

>Yes, all Zama’s technologies are patented.

**Can you customize a solution for my specific use case?**

>We are open to collaborating and advancing the FHE space with our partners. If you have specific needs, please email us at [email protected].



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## Support



  

  

  



🌟 If you find this project helpful or interesting, please consider giving it a star on GitHub! Your support helps to grow the community and motivates further development.



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