Awesome

TFHE-rs is a pure Rust implementation of TFHE for boolean and integer arithmetics over encrypted data. It includes:

• a Rust API
• a C API
• and a client-side WASM API

TFHE-rs is meant for developers and researchers who want full control over what they can do with TFHE, while not having to worry about the low level implementation. The goal is to have a stable, simple, high-performance, and production-ready library for all the advanced features of TFHE.

Getting Started

The steps to run a first example are described below.

Cargo.toml configuration

To use the latest version of TFHE-rs in your project, you first need to add it as a dependency in your Cargo.toml:

• For x86_64-based machines running Unix-like OSes:

tfhe = { version = "*", features = ["boolean", "shortint", "integer", "x86_64-unix"] }

• For Apple Silicon or aarch64-based machines running Unix-like OSes:

tfhe = { version = "*", features = ["boolean", "shortint", "integer", "aarch64-unix"] }

Note: users with ARM devices must compile TFHE-rs using a stable toolchain with version >= 1.72.

• For x86_64-based machines with the rdseed instruction running Windows:

tfhe = { version = "*", features = ["boolean", "shortint", "integer", "x86_64"] }

Note: aarch64-based machines are not yet supported for Windows as it's currently missing an entropy source to be able to seed the CSPRNGs used in TFHE-rs

A simple example

Here is a full example:

use tfhe::prelude::*;
use tfhe::{generate_keys, set_server_key, ConfigBuilder, FheUint32, FheUint8};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Basic configuration to use homomorphic integers
    let config = ConfigBuilder::default().build();

    // Key generation
    let (client_key, server_keys) = generate_keys(config);

    let clear_a = 1344u32;
    let clear_b = 5u32;
    let clear_c = 7u8;

    // Encrypting the input data using the (private) client_key
    // FheUint32: Encrypted equivalent to u32
    let mut encrypted_a = FheUint32::try_encrypt(clear_a, &client_key)?;
    let encrypted_b = FheUint32::try_encrypt(clear_b, &client_key)?;

    // FheUint8: Encrypted equivalent to u8
    let encrypted_c = FheUint8::try_encrypt(clear_c, &client_key)?;

    // On the server side:
    set_server_key(server_keys);

    // Clear equivalent computations: 1344 * 5 = 6720
    let encrypted_res_mul = &encrypted_a * &encrypted_b;

    // Clear equivalent computations: 1344 >> 5 = 42
    encrypted_a = &encrypted_res_mul >> &encrypted_b;

    // Clear equivalent computations: let casted_a = a as u8;
    let casted_a: FheUint8 = encrypted_a.cast_into();

    // Clear equivalent computations: min(42, 7) = 7
    let encrypted_res_min = &casted_a.min(&encrypted_c);

    // Operation between clear and encrypted data:
    // Clear equivalent computations: 7 & 1 = 1
    let encrypted_res = encrypted_res_min & 1_u8;

    // Decrypting on the client side:
    let clear_res: u8 = encrypted_res.decrypt(&client_key);
    assert_eq!(clear_res, 1_u8);

    Ok(())
}

To run this code, use the following command:

Note that when running code that uses tfhe-rs, it is highly recommended to run in release mode with cargo's --release flag to have the best performances possible,

Contributing

There are two ways to contribute to TFHE-rs:

• you can open issues to report bugs or typos, or to suggest new ideas
• you can ask to become an official contributor by emailing hello@zama.ai. (becoming an approved contributor involves signing our Contributor License Agreement (CLA))

Only approved contributors can send pull requests, so please make sure to get in touch before you do!

Credits

This library uses several dependencies and we would like to thank the contributors of those libraries.

Need support?

Citing TFHE-rs

To cite TFHE-rs in academic papers, please use the following entry:

@Misc{TFHE-rs,
  title={{TFHE-rs: A Pure Rust Implementation of the TFHE Scheme for Boolean and Integer Arithmetics Over Encrypted Data}},
  author={Zama},
  year={2022},
  note={\url{https://github.com/zama-ai/tfhe-rs}},
}

License

This software is distributed under the BSD-3-Clause-Clear license. If you have any questions, please contact us at hello@zama.ai.

Disclaimers

Security Estimation

Security estimations are done using the Lattice Estimator with red_cost_model = reduction.RC.BDGL16.

When a new update is published in the Lattice Estimator, we update parameters accordingly.

Side-Channel Attacks

Mitigation for side channel attacks have not yet been implemented in TFHE-rs, and will be released in upcoming versions.