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39 lines
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39 lines
3.0 KiB
Markdown
# What is TFHE-rs?
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<mark style="background-color:yellow;">⭐️</mark> [<mark style="background-color:yellow;">Star the repo on Github</mark>](https://github.com/zama-ai/tfhe-rs) <mark style="background-color:yellow;">| 🗣</mark> [<mark style="background-color:yellow;">Community support forum</mark> ](https://community.zama.ai)<mark style="background-color:yellow;">| 📁</mark> [<mark style="background-color:yellow;">Contribute to the project</mark>](https://docs.zama.ai/tfhe-rs/developers/contributing)<mark style="background-color:yellow;"></mark>
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TFHE-rs is a pure Rust implementation of TFHE for boolean and small integer arithmetics over encrypted data. It includes a Rust and C API, as well as a client-side WASM API.
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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.
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The goal is to have a stable, simple, high-performance, and production-ready library for all the advanced features of TFHE.
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### Key Cryptographic concepts
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TFHE-rs library implements Zama’s variant of Fully Homomorphic Encryption over the Torus (TFHE). TFHE is based on Learning With Errors (LWE), a well studied cryptographic primitive believed to be secure even against quantum computers.
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In cryptography, a raw value is called a message (also sometimes called a cleartext), an encoded message is called a plaintext and an encrypted plaintext is called a ciphertext.
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The idea of homomorphic encryption is that you can compute on ciphertexts while not knowing messages encrypted in them. A scheme is said to be _fully homomorphic_, meaning any program can be evaluated with it, if at least two of the following operations are supported \($$x$$is a plaintext and $$E[x]$$ is the
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corresponding ciphertext\):
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* homomorphic univariate function evaluation: $$f(E[x]) = E[f(x)]$$
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* homomorphic addition: $$E[x] + E[y] = E[x + y]$$
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* homomorphic multiplication: $$E[x] * E[y] = E[x * y]$$
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Zama's variant of TFHE is fully homomorphic and deals with fixed-precision numbers as messages. It implements all needed homomorphic operations, such as addition and function evaluation via **Programmable Bootstrapping**. You can read more about Zama's TFHE variant in the [preliminary whitepaper](https://whitepaper.zama.ai/).
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Using FHE in a Rust program with TFHE-rs consists in:
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* generating a client key and a server key using secure parameters:
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* client key encrypts/decrypts data and must be kept secret
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* server key is used to perform operations on encrypted data and could be
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public (also called evaluation key)
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* encrypting plaintexts using the client key to produce ciphertexts
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* operating homomorphically on ciphertexts with the server key
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* decrypting the resulting ciphertexts into plaintexts using the client key
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If you would like to know more about the problems that FHE solves, we suggest you review our [6 minute introduction to homomorphic encryption](https://6min.zama.ai/).
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