TFHE-rs is a pure Rust implementation of TFHE for Boolean and integer arithmetics over encrypted data. It includes a Rust and C API, as well as a client-side WASM API.
TFHE-rs also includes a GPU accelerated backend as well as an HPU accelerated backend.
Learn the basics of TFHE-rs, set it up, and make it run with ease.
Start building with TFHE-rs by exploring its core features, discovering essential guides, and learning more with user-friendly tutorials.
Access to additional resources and join the Zama community.
Explore step-by-step guides that walk you through real-world uses of TFHE-rs.
Homomorphic parity bit: Learn how to implement a parity bit calculation over encrypted data
Homomorphic case changing on ASCII string: See how to process string data securely by changing cases while keeping the data encrypted.
SHA256 with Boolean API: Delve into a more complex example: implementing the SHA256 hash function entirely on encrypted boolean values.
All tutorials: A complete list of all available tutorials in one place.tutorials: A complete list of all available tutorials in one place.
Take a deep dive into TFHE-rs, exploring APIs from the highest to the lowest level of abstraction and accessing additional resources for in-depth explanations.
Rust API reference: High-level API that abstracts cryptographic complexities and simplifies the development and more
Fine-grained APIs: Mid-level APIs that enable evaluation of Boolean, short integer, and integer circuits
Core crypto API: Low-level API with the primitive functions and types of the TFHE scheme
TFHE deep dive: Resources that explain the Fully Homomorphic Encryption scheme - TFHE
TFHE-rs handbook: Document describing algorithms implemented in TFHE-rs
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What is TFHE-rs?
Understand TFHE-rs library and basic cryptographic concepts
Installation
Follow the step by step guide to import TFHE-rs in your project
Quick start
See a full example of using TFHE-rs to compute on encrypted data
FHE Computations
Run FHE computation on encrypted data.
Configuration
Advanced configuration for better performance.
Integration
Use TFHE-rs in different contexts or platforms..
