TFHE-rs

https://github.com/zama-ai/tfhe-rs
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TFHE-rs: A Pure Rust implementation of the TFHE Scheme for Boolean and Integer Arithmetics Over...
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### Install and Initialize Rust Project with Cargo

Source: https://github.com/zama-ai/tfhe-rs/blob/main/tfhe/docs/getting-started/quick-start.md

Demonstrates using Cargo, the Rust build and package manager, to verify the installation and create a new binary application project. This is the first step in setting up a Rust environment for TFHE-rs.

```console
$ cargo --version
cargo 1.81.0 (2dbb1af80 2024-08-20)
$ cargo new tfhe-example
    Creating binary (application) `tfhe-example` package
note: see more `Cargo.toml` keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
```

--------------------------------

### High-Level API Example

Source: https://github.com/zama-ai/tfhe-rs/blob/main/tfhe/docs/references/fine-grained-apis/quick-start.md

Demonstrates the high-level API for homomorphic addition of unsigned 8-bit integers. It covers key generation, encryption, homomorphic addition using overloaded operators, and decryption. This example requires running with the `--release` flag.

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

fn main() {
    let config = ConfigBuilder::default()
        .build();

    let (client_key, server_key) = generate_keys(config);

    set_server_key(server_key);

    let clear_a = 27u8;
    let clear_b = 128u8;

    let a = FheUint8::encrypt(clear_a, &client_key);
    let b = FheUint8::encrypt(clear_b, &client_key);

    let result = a + b;

    let decrypted_result: u8 = result.decrypt(&client_key);

    let clear_result = clear_a + clear_b;

    assert_eq!(decrypted_result, clear_result);
}
```

--------------------------------

### TFHE-rs High-Level API Usage Example

Source: https://github.com/zama-ai/tfhe-rs/blob/main/tfhe/docs/getting-started/quick-start.md

A complete Rust code example demonstrating the TFHE-rs high-level API workflow. It covers key generation, encrypting two `FheUint8` values, performing homomorphic addition on the server-side, and decrypting the result back on the client-side, with an assertion to verify correctness.

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

fn main() {
    let config = ConfigBuilder::default().build();

    // Client-side
    let (client_key, server_key) = generate_keys(config);

    let clear_a = 27u8;
    let clear_b = 128u8;

    let a = FheUint8::encrypt(clear_a, &client_key);
    let b = FheUint8::encrypt(clear_b, &client_key);

    //Server-side
    set_server_key(server_key);
    let result = a + b;

    //Client-side
    let decrypted_result: u8 = result.decrypt(&client_key);

    let clear_result = clear_a + clear_b;

    assert_eq!(decrypted_result, clear_result);
}
```

--------------------------------

### Shortint Arithmetic Example

Source: https://github.com/zama-ai/tfhe-rs/blob/main/tfhe/docs/references/fine-grained-apis/quick-start.md

Provides an example of performing homomorphic addition with short integers using the `tfhe::shortint` module. It demonstrates key generation with specific parameters (2 bits message, 2 bits carry), encrypting integer messages, executing homomorphic addition on ciphertexts, and decrypting the result. This example requires running with the `--release` flag.

```rust
use tfhe::shortint::prelude::*;

fn main() {
    // We generate a set of client/server keys
    // using parameters with 2 bits of message and 2 bits of carry
    let (client_key, server_key) = gen_keys(PARAM_MESSAGE_2_CARRY_2);

    let msg1 = 1;
    let msg2 = 0;

    let modulus = client_key.parameters().message_modulus().0;

    // We use the client key to encrypt two messages:
    let ct_1 = client_key.encrypt(msg1);
    let ct_2 = client_key.encrypt(msg2);

    // We use the server public key to execute an integer circuit:
    let ct_3 = server_key.add(&ct_1, &ct_2);

    // We use the client key to decrypt the output of the circuit:
    let output = client_key.decrypt(&ct_3);
    assert_eq!(output, (msg1 + msg2) % modulus);
}
```

--------------------------------

### Boolean Circuit Evaluation Example

Source: https://github.com/zama-ai/tfhe-rs/blob/main/tfhe/docs/references/fine-grained-apis/quick-start.md

Illustrates evaluating a Boolean circuit using the `tfhe::boolean` module. It shows how to encrypt boolean values, perform logical operations (NOT, AND, NAND, MUX) on ciphertexts using the server key, and decrypt the final result. This example requires running with the `--release` flag.

```rust
use tfhe::boolean::prelude::*;

fn main() {
    // We generate a set of client/server keys, using the default parameters:
    let (client_key, server_key) = gen_keys();

    // We use the client secret key to encrypt two messages:
    let ct_1 = client_key.encrypt(true);
    let ct_2 = client_key.encrypt(false);

    // We use the server public key to execute a boolean circuit:
    // if ((NOT ct_2) NAND (ct_1 AND ct_2)) then (NOT ct_2) else (ct_1 AND ct_2)
    let ct_3 = server_key.not(&ct_2);
    let ct_4 = server_key.and(&ct_1, &ct_2);
    let ct_5 = server_key.nand(&ct_3, &ct_4);
    let ct_6 = server_key.mux(&ct_5, &ct_3, &ct_4);

    // We use the client key to decrypt the output of the circuit:
    let output = client_key.decrypt(&ct_6);
    assert!(output);
}
```

--------------------------------

### Full HPU Acceleration Example

Source: https://github.com/zama-ai/tfhe-rs/blob/main/tfhe/docs/configuration/hpu-acceleration/run-on-hpu.md

A complete Rust program demonstrating the setup and usage of the TFHE-rs HPU backend. It covers device instantiation, key generation, encryption, homomorphic addition, and decryption.

```rust
use tfhe::{Config, set_server_key, FheUint8, ClientKey, CompressedServerKey};
use tfhe::prelude::*;
use tfhe::tfhe_hpu_backend::prelude::*;

fn main() {

    // Instantiate HpuDevice --------------------------------------------------
    // HPU configuration knobs are retrieved from a TOML configuration file. Prebuilt configurations could be find in `backends/tfhe-hpu-backend/config_store`
    // For ease of use a setup_hpu.sh script is available in repository root folder and it handle the required environment variables setup and driver initialisation
    // More details are available in `backends/tfhe-hpu-backend/README.md`
    let hpu_device = HpuDevice::from_config(ShellString::new("${HPU_BACKEND_DIR}/config_store/${HPU_CONFIG}/hpu_config.toml".to_string()).expand().as_str());

    // Generate keys ----------------------------------------------------------
    let config = Config::from_hpu_device(&hpu_device);

    let client_key = ClientKey::generate(config);
    let compressed_server_key = CompressedServerKey::new(&client_key);

    // Register HpuDevice and key as thread-local engine
    set_server_key((hpu_device, compressed_server_key));

    let clear_a = 27u8;
    let clear_b = 128u8;

    let a = FheUint8::encrypt(clear_a, &client_key);
    let b = FheUint8::encrypt(clear_b, &client_key);

    // Server-side computation
    let result = a + b;

    // Client-side
    let decrypted_result: u8 = result.decrypt(&client_key);

    let clear_result = clear_a + clear_b;

    assert_eq!(decrypted_result, clear_result);
}
```

--------------------------------

### Build and Run HPU Examples

Source: https://github.com/zama-ai/tfhe-rs/blob/main/backends/tfhe-hpu-backend/README.md

Builds the tfhe-rs project with HPU features and runs pre-made examples like hpu_hlapi and hpu_bench. Requires setting up the HPU environment and potentially pulling HPU files.

```bash
cargo build --release --features="hpu-v80" --example hpu_hlapi --example hpu_bench
source setup_hpu.sh --config v80
./target/release/examples/hpu_bench --integer-w 64 --integer-w 32 --iop MUL --iter 10
./target/release/examples/hpu_hlapi
```

--------------------------------

### Rust LWE Encryption and Decryption Example

Source: https://github.com/zama-ai/tfhe-rs/blob/main/tfhe/docs/references/core-crypto-api/presentation.md

Demonstrates the process of encrypting and decrypting a message using LWE ciphertext primitives from the tfhe-rs core_crypto module. It covers parameter setup, key generation, plaintext creation, encryption, decryption, and message recovery.

```rust
use tfhe::core_crypto::prelude::*;

// DISCLAIMER: these toy example parameters are not guaranteed to be secure or yield correct
// computations
// Define parameters for LweCiphertext creation
let lwe_dimension = LweDimension(742);
let lwe_noise_distribution =
    Gaussian::from_dispersion_parameter(StandardDev(0.000007069849454709433), 0.0);
let ciphertext_modulus = CiphertextModulus::new_native();

// Create the PRNG
let mut seeder = new_seeder();
let seeder = seeder.as_mut();
let mut encryption_generator =
    EncryptionRandomGenerator::<DefaultRandomGenerator>::new(seeder.seed(), seeder);
let mut secret_generator =
    SecretRandomGenerator::<DefaultRandomGenerator>::new(seeder.seed());

// Create the LweSecretKey
let lwe_secret_key =
    allocate_and_generate_new_binary_lwe_secret_key(lwe_dimension, &mut secret_generator);

// Create the plaintext
let msg = 3u64;
let plaintext = Plaintext(msg << 60);

// Create a new LweCiphertext
let mut lwe = LweCiphertext::new(0u64, lwe_dimension.to_lwe_size(), ciphertext_modulus);

encrypt_lwe_ciphertext(
    &lwe_secret_key,
    &mut lwe,
    plaintext,
    lwe_noise_distribution,
    &mut encryption_generator,
);

let decrypted_plaintext = decrypt_lwe_ciphertext(&lwe_secret_key, &lwe);

// Round and remove encoding
// First create a decomposer working on the high 4 bits corresponding to our encoding.
let decomposer = SignedDecomposer::new(DecompositionBaseLog(4), DecompositionLevelCount(1));
let rounded = decomposer.closest_representable(decrypted_plaintext.0);

// Remove the encoding
let cleartext = rounded >> 60;

// Check we recovered the original message
assert_eq!(cleartext, msg);

```

--------------------------------

### Rust Integer Encryption and Homomorphic Max

Source: https://github.com/zama-ai/tfhe-rs/blob/main/tfhe/docs/references/fine-grained-apis/quick-start.md

Demonstrates encrypting 16-bit integers using the TFHE library's radix encoding. It shows how to generate keys, encrypt two unsigned 16-bit integers, perform a homomorphic maximum operation on them using `smart_max_parallelized`, and then decrypt the result. This example utilizes `tfhe::integer::gen_keys_radix` and `tfhe::shortint::parameters::PARAM_MESSAGE_2_CARRY_2`. Note: Run with the `--release` flag.

```rust
use tfhe::integer::gen_keys_radix;
use tfhe::shortint::parameters::PARAM_MESSAGE_2_CARRY_2;

fn main() {
    // We generate keys to encrypt 16 bits radix-encoded integers
    // using 8 blocks of 2 bits
    let (cks, sks) = gen_keys_radix(PARAM_MESSAGE_2_CARRY_2, 8);

    let clear_a = 2382u16;
    let clear_b = 29374u16;

    let mut a = cks.encrypt(clear_a as u64);
    let mut b = cks.encrypt(clear_b as u64);

    let encrypted_max = sks.smart_max_parallelized(&mut a, &mut b);
    let decrypted_max: u64 = cks.decrypt(&encrypted_max);

    assert_eq!(decrypted_max as u16, clear_a.max(clear_b))
}
```

--------------------------------

### Add TFHE-rs Dependency to Cargo.toml

Source: https://github.com/zama-ai/tfhe-rs/blob/main/tfhe/docs/getting-started/quick-start.md

Illustrates how to modify the `Cargo.toml` file to include TFHE-rs as a project dependency. It specifies the version and enables the 'integer' feature, which is necessary for integer-based homomorphic computations.

```toml
[package]
name = "tfhe-example"
version = "0.1.0"
edition = "2021"

[dependencies]
tfhe = { version = "~1.3.0", features = ["integer"] }
```

--------------------------------

### Command: Run TFHE-rs Example

Source: https://github.com/zama-ai/tfhe-rs/blob/main/README.md

This command is used to compile and run the TFHE-rs Rust example. It is highly recommended to use the `--release` flag for optimal performance, as homomorphic encryption operations are computationally intensive.

```bash
cargo run --release
```

--------------------------------

### Setup HPU Mockup Environment

Source: https://github.com/zama-ai/tfhe-rs/blob/main/mockups/tfhe-hpu-mockup/README.md

Sources the setup script to configure the HPU mockup environment. This is a prerequisite for running mockup applications and tests.

```bash
source setup_hpu.sh --config sim
```

--------------------------------

### tfhe-rs: Homomorphic Doubling Example (Cleartext & PBS Setup)

Source: https://github.com/zama-ai/tfhe-rs/blob/main/tfhe/docs/references/core-crypto-api/tutorial.md

This Rust code example from tfhe-rs demonstrates homomorphic computation of 2 * 3. It initializes cryptographic parameters, generates LWE and GLWE keys, and creates a bootstrapping key. The code then encrypts the message 3, performs a cleartext multiplication by 2, and decrypts the result. It also sets up the necessary components for a homomorphic multiplication using Programmable Bootstrapping (PBS).

```rust
use tfhe::core_crypto::prelude::*;

pub fn main() {
    // DISCLAIMER: these toy example parameters are not guaranteed to be secure or yield correct
    // computations
    // Define the parameters for a 4 bits message able to hold the doubled 2 bits message
    let small_lwe_dimension = LweDimension(742);
    let glwe_dimension = GlweDimension(1);
    let polynomial_size = PolynomialSize(2048);
    let lwe_noise_distribution =
        Gaussian::from_dispersion_parameter(StandardDev(0.000007069849454709433), 0.0);
    let glwe_noise_distribution =
        Gaussian::from_dispersion_parameter(StandardDev(0.00000000000000029403601535432533), 0.0);
    let pbs_base_log = DecompositionBaseLog(23);
    let pbs_level = DecompositionLevelCount(1);
    let ciphertext_modulus = CiphertextModulus::new_native();

    // Request the best seeder possible, starting with hardware entropy sources and falling back to
    // /dev/random on Unix systems if enabled via cargo features
    let mut boxed_seeder = new_seeder();
    // Get a mutable reference to the seeder as a trait object from the Box returned by new_seeder
    let seeder = boxed_seeder.as_mut();

    // Create a generator which uses a CSPRNG to generate secret keys
    let mut secret_generator =
        SecretRandomGenerator::<DefaultRandomGenerator>::new(seeder.seed());

    // Create a generator which uses two CSPRNGs to generate public masks and secret encryption
    // noise
    let mut encryption_generator =
        EncryptionRandomGenerator::<DefaultRandomGenerator>::new(seeder.seed(), seeder);

    println!("Generating keys...");

    // Generate an LweSecretKey with binary coefficients
    let small_lwe_sk =
        LweSecretKey::generate_new_binary(small_lwe_dimension, &mut secret_generator);

    // Generate a GlweSecretKey with binary coefficients
    let glwe_sk =
        GlweSecretKey::generate_new_binary(glwe_dimension, polynomial_size, &mut secret_generator);

    // Create a copy of the GlweSecretKey re-interpreted as an LweSecretKey
    let big_lwe_sk = glwe_sk.clone().into_lwe_secret_key();

    // Generate the bootstrapping key, we use the parallel variant for performance reason
    let std_bootstrapping_key = par_allocate_and_generate_new_lwe_bootstrap_key(
        &small_lwe_sk,
        &glwe_sk,
        pbs_base_log,
        pbs_level,
        glwe_noise_distribution,
        ciphertext_modulus,
        &mut encryption_generator,
    );

    // Create the empty bootstrapping key in the Fourier domain
    let mut fourier_bsk = FourierLweBootstrapKey::new(
        std_bootstrapping_key.input_lwe_dimension(),
        std_bootstrapping_key.glwe_size(),
        std_bootstrapping_key.polynomial_size(),
        std_bootstrapping_key.decomposition_base_log(),
        std_bootstrapping_key.decomposition_level_count(),
    );

    // Use the conversion function (a memory optimized version also exists but is more complicated
    // to use) to convert the standard bootstrapping key to the Fourier domain
    convert_standard_lwe_bootstrap_key_to_fourier(&std_bootstrapping_key, &mut fourier_bsk);
    // We don't need the standard bootstrapping key anymore
    drop(std_bootstrapping_key);

    // Our 4 bits message space
    let message_modulus = 1u64 << 4;

    // Our input message
    let input_message = 3u64;

    // Delta used to encode 4 bits of message + a bit of padding on u64
    let delta = (1_u64 << 63) / message_modulus;

    // Apply our encoding
    let plaintext = Plaintext(input_message * delta);

    // Allocate a new LweCiphertext and encrypt our plaintext
    let lwe_ciphertext_in: LweCiphertextOwned<u64> = allocate_and_encrypt_new_lwe_ciphertext(
        &small_lwe_sk,
        plaintext,
        lwe_noise_distribution,
        ciphertext_modulus,
        &mut encryption_generator,
    );

    // Compute a cleartext multiplication by 2
    let mut cleartext_multiplication_ct = lwe_ciphertext_in.clone();
    println!("Performing cleartext multiplication...");
    lwe_ciphertext_cleartext_mul(
        &mut cleartext_multiplication_ct,
        &lwe_ciphertext_in,
        Cleartext(2),
    );

    // Decrypt the cleartext multiplication result
    let cleartext_multiplication_plaintext: Plaintext<u64> = 
        decrypt_lwe_ciphertext(&small_lwe_sk, &cleartext_multiplication_ct);

    // Create a SignedDecomposer to perform the rounding of the decrypted plaintext
    // We pass a DecompositionBaseLog of 5 and a DecompositionLevelCount of 1 indicating we want to
    // round the 5 MSB, 1 bit of padding plus our 4 bits of message
    let signed_decomposer = 

```

--------------------------------

### Running the TFHE-rs Example

Source: https://github.com/zama-ai/tfhe-rs/blob/main/tfhe/docs/integration/js-on-wasm-api.md

Command to execute the JavaScript example script using Node.js after the TFHE-rs WASM package has been built and the example code is saved in a file (e.g., `example.js`).

```shell
node example.js
```

--------------------------------

### Reproduce TFHE-rs Benchmarks

Source: https://github.com/zama-ai/tfhe-rs/blob/main/tfhe/docs/getting-started/benchmarks/cpu/cpu-programmable-bootstrapping.md

Instructions to run programmable bootstrapping (PBS) and keyswitch-PBS (KS-PBS) benchmarks for TFHE-rs. These commands utilize the 'make' utility to compile and execute the benchmark suites.

```shell
#PBS benchmarks:
make bench_pbs

#KS-PBS benchmarks:
make bench_ks_pbs
```

--------------------------------

### Setup and Run HPU Benchmarks

Source: https://github.com/zama-ai/tfhe-rs/blob/main/backends/tfhe-hpu-backend/README.md

Sets up the HPU environment and initiates performance benchmarks for tfhe-rs. This includes benchmarks for the High-Level API (hlapi) and Integer API, with specific targets for different benchmark types.

```bash
# Do not forget to correctly set environment before hand
source setup_hpu.sh --config v80 --init-qdma

# Run hlapi benches
make test_high_level_api_hpu

# Run hlapi erc20 benches
make bench_hlapi_erc20_hpu 

# Run integer level benches
make bench_integer_hpu
```

--------------------------------

### Complete Example: Parity Bit Calculation with FHE and Clear Values

Source: https://github.com/zama-ai/tfhe-rs/blob/main/tfhe/docs/tutorials/parity-bit.md

This example demonstrates the usage of the generic `compute_parity_bit` function with both clear boolean values and TFHE encrypted booleans (`FheBool`). It includes key generation, server key setup, and the application of the parity bit logic to different data types.

```Rust
use tfhe::{FheBool, ConfigBuilder, generate_keys, set_server_key};
use tfhe::prelude::*;
use std::ops::{Not, BitXor};

#[derive(Copy, Clone, Debug)]
enum ParityMode {
    // The sum bits of message + parity bit must an odd number
    Odd,
    // The sum bits of message + parity bit must an even number
    Even,
}

fn compute_parity_bit<BoolType>(fhe_bits: &[BoolType], mode: ParityMode) -> BoolType
    where
        BoolType: Clone + Not<Output=BoolType>,
        for<'a> &'a BoolType: BitXor<BoolType, Output=BoolType>,
{
    let mut parity_bit = fhe_bits[0].clone();
    for fhe_bit in &fhe_bits[1..] {
        parity_bit = fhe_bit ^ parity_bit
    }

    match mode {
        ParityMode::Odd => !parity_bit,
        ParityMode::Even => parity_bit,
    }
}

fn is_even(n: u8) -> bool {
    (n & 1) == 0
}

fn is_odd(n: u8) -> bool {
    !is_even(n)
}

fn check_parity_bit_validity(bits: &[bool], mode: ParityMode, parity_bit: bool) -> bool {
    let num_bit_set = bits
        .iter()
        .map(|bit| *bit as u8)
        .fold(parity_bit as u8, |acc, bit| acc + bit);

    match mode {
        ParityMode::Even => is_even(num_bit_set),
        ParityMode::Odd => is_odd(num_bit_set),
    }
}

fn main() {
    let config = ConfigBuilder::default().build();

    let (client_key, server_key) = generate_keys(config);

    set_server_key(server_key);

    let clear_bits = [0, 1, 0, 0, 0, 1, 1].map(|b| (b != 0));

    let fhe_bits = clear_bits
}
```

--------------------------------

### Build and Run User Application (HPU Bench)

Source: https://github.com/zama-ai/tfhe-rs/blob/main/mockups/tfhe-hpu-mockup/README.md

Compiles and runs a user application example, such as `hpu_bench`, with HPU support enabled. It demonstrates how to specify integer width and IO operations.

```bash
# Example of CLI for building/running hpu_bench application
# For convenienc, `just hpu_bench` could be also used
cargo build --release --features="hpu" --example hpu_bench
# Start MUL and ADD IOp on 64b integer
./target/release/examples/hpu_bench --integer-w 64 --iop MUL --iop ADD
```

--------------------------------

### Add TFHE-rs Dependency (Standard)

Source: https://github.com/zama-ai/tfhe-rs/blob/main/tfhe/docs/getting-started/installation.md

Include the TFHE-rs crate in your Rust project's Cargo.toml file. This configuration enables the 'boolean', 'shortint', and 'integer' features for core TFHE functionalities.

```toml
tfhe = { version = "~1.3.0", features = ["boolean", "shortint", "integer"] }
```

--------------------------------

### Build and Run Mockup Application

Source: https://github.com/zama-ai/tfhe-rs/blob/main/mockups/tfhe-hpu-mockup/README.md

Compiles the HPU mockup binary in release mode and runs it with specified parameter files and optional knobs for debugging and performance analysis.

```bash
# Example of CLI for building/running mockup application
# For convenience, `just mockup` could be also used
cargo build --release --bin hpu_mockup
./target/release/hpu_mockup \
  --params mockups/tfhe-hpu-mockup/params/gaussian_64b_fast.ron \
  [--freq-hz --register --isc-depth]
  [--dump-out mockup_out/ --dump-reg]\
  [--report-out mockup_rpt/ --report-trace]
```

--------------------------------

### Add TFHE-rs Dependency (Software PRNG)

Source: https://github.com/zama-ai/tfhe-rs/blob/main/tfhe/docs/getting-started/installation.md

Configure your Rust project's Cargo.toml to include TFHE-rs with the 'software-prng' feature. This is recommended for systems without hardware AES acceleration, ensuring broader compatibility.

```toml
tfhe = { version = "~1.3.0", features = ["boolean", "shortint", "integer", "software-prng"] }
```

--------------------------------

### Filter tfhe-rs CUDA Backend Tests

Source: https://github.com/zama-ai/tfhe-rs/blob/main/backends/tfhe-cuda-backend/cuda/tests_and_benchmarks/tests/README.md

How to run a subset of tests by specifying a filter for test names. This example shows running tests starting with 'Bootstrap'. The `--gtest_list_tests` flag can be used to list all available tests.

```bash
tests/src/test_tfhe_cuda_backend --gtest_filter=Bootstrap*
```

--------------------------------

### TFHE-rs GPU Key Generation and Setup

Source: https://github.com/zama-ai/tfhe-rs/blob/main/tfhe/docs/configuration/gpu-acceleration/simple-example.md

Illustrates the process of generating a compressed server key and decompressing it to the GPU for accelerated computations. This involves creating a client key, a compressed server key, and then transferring the server key to the GPU.

```rust
    let client_key= ClientKey::generate(config);
    let compressed_server_key = CompressedServerKey::new(&client_key);

    let gpu_key = compressed_server_key.decompress_to_gpu();
```

--------------------------------

### TriviumStreamShortint Example

Source: https://github.com/zama-ai/tfhe-rs/blob/main/apps/trivium/README.md

Demonstrates the usage of TriviumStreamShortint with tfhe-rs for FHE shortint (bit-level) operations. It shows key generation, parameter setup, initialization of the Trivium stream cipher, and trans-ciphering a message using `trans_encrypt_64`.

```rust
use tfhe::shortint::prelude::*;
use tfhe::shortint::parameters::current_params::{
    V1_3_PARAM_MESSAGE_1_CARRY_1_KS_PBS_GAUSSIAN_2M128,
    V1_3_PARAM_MESSAGE_2_CARRY_2_KS_PBS_GAUSSIAN_2M128,
    V1_3_PARAM_KEYSWITCH_1_1_KS_PBS_TO_2_2_KS_PBS_GAUSSIAN_2M128_2M128
};
use tfhe::{ConfigBuilder, generate_keys, FheUint64};
use tfhe::prelude::*;
use tfhe_trivium::TriviumStreamShortint;

fn test_shortint() {
    // Setup for high-level API (e.g., FheUint64)
    let config = ConfigBuilder::default()
        .use_custom_parameters(V1_3_PARAM_MESSAGE_2_CARRY_2_KS_PBS_GAUSSIAN_2M128)
        .build();
    let (hl_client_key, hl_server_key) = generate_keys(config);
    let underlying_ck: tfhe::shortint::ClientKey = (*hl_client_key.as_ref()).clone().into();
    let underlying_sk: tfhe::shortint::ServerKey = (*hl_server_key.as_ref()).clone().into();

    // Setup for low-level shortint API
    let (client_key, server_key): (ClientKey, ServerKey) = gen_keys(V1_3_PARAM_MESSAGE_1_CARRY_1_KS_PBS_GAUSSIAN_2M128);
    
    // Key switching key for parameter transformation
    let ksk = KeySwitchingKey::new(
        (&client_key, Some(&server_key)),
        (&underlying_ck, &underlying_sk),
        V1_3_PARAM_KEYSWITCH_1_1_KS_PBS_TO_2_2_KS_PBS_GAUSSIAN_2M128_2M128
    );

    // Example key and IV strings (hexadecimal)
    let key_string = "0053A6F94C9FF24598EB".to_string();
    let mut key = [0u8; 80]; // Assuming 80 bits for this example

    for i in (0..key_string.len()).step_by(2) {
        let mut val = u64::from_str_radix(&key_string[i..i+2], 16).unwrap();
        for j in 0..8 {
            key[8*(i>>1) + j] = (val % 2) as u8;
            val >>= 1;
        }
    }

    let iv_string = "0D74DB42A91077DE45AC".to_string();
    let mut iv = [0u8; 80]; // Assuming 80 bits for this example

    for i in (0..iv_string.len()).step_by(2) {
        let mut val = u64::from_str_radix(&iv_string[i..i+2], 16).unwrap();
        for j in 0..8 {
            iv[8*(i>>1) + j] = (val % 2) as u8;
            val >>= 1;
        }
    }
    
    // Expected output for verification
    let output_0_63    = "F4CD954A717F26A7D6930830C4E7CF0819F80E03F25F342C64ADC66ABA7F8A8E6EAA49F23632AE3CD41A7BD290A0132F81C6D4043B6E397D7388F3A03B5FE358".to_string();

    // Encrypt key and IV using the high-level client key
    let cipher_key = key.map(|x| hl_client_key.encrypt(x));
    let cipher_iv = iv.map(|x| hl_client_key.encrypt(x));

    // Initialize a vector of FheUint64 ciphertexts for the message
    let mut ciphered_message = vec![FheUint64::try_encrypt(0u64, &hl_client_key).unwrap(); 9];

    // Initialize the Trivium stream cipher with encrypted key, IV, server key, and key switching key
    let mut trivium = TriviumStreamShortint::new(cipher_key, cipher_iv, &server_key, &ksk);

    // Process the message using trans_encrypt_64
    let mut vec = Vec::<u64>::with_capacity(8);
    while vec.len() < 8 {
        let trans_ciphered_message = trivium.trans_encrypt_64(ciphered_message.pop().unwrap(), &hl_server_key);
        vec.push(trans_ciphered_message.decrypt(&hl_client_key));
    }

    // Helper function to convert vector of u64 to hex string (assumed to exist)
    // let hexadecimal = get_hexagonal_string_from_u64(vec);
    // assert_eq!(output_0_63, hexadecimal[0..64*2]);
}

// Placeholder for the helper function if it were part of the snippet
// fn get_hexagonal_string_from_u64(data: Vec<u64>) -> String {
//     let mut hex_string = String::new();
//     for val in data {
//         hex_string.push_str(&format!("{:016X}", val));
//     }
//     hex_string
// }

```

--------------------------------

### Build TFHE Cuda Backend

Source: https://github.com/zama-ai/tfhe-rs/blob/main/backends/tfhe-cuda-backend/README.md

Instructions to clone the repository, navigate to the CUDA backend directory, create a build directory, configure with CMake, and compile the project using make.

```bash
git clone git@github.com:zama-ai/tfhe-rs
cd backends/tfhe-cuda-backend/cuda
mkdir build
cd build
cmake ..
make
```

--------------------------------

### TFHE-rs Zero-Knowledge Proof Example

Source: https://github.com/zama-ai/tfhe-rs/blob/main/tfhe/docs/fhe-computation/advanced-features/zk-pok.md

Demonstrates the client-side encryption and proof generation, and server-side verification and computation using TFHE-rs' zero-knowledge proof feature. It covers parameter setup, CRS generation, ciphertext encryption with proof, and decryption of the result.

```rust
use rand::prelude::*;\nuse tfhe::prelude::*;\nuse tfhe::set_server_key;\nuse tfhe::zk::{CompactPkeCrs, ZkComputeLoad};\n\npub fn main() -> Result<(), Box<dyn std::error::Error>> {\n    let mut rng = thread_rng();\n\n    let params = tfhe::shortint::parameters::PARAM_MESSAGE_2_CARRY_2_KS_PBS_TUNIFORM_2M128;\n    // Indicate which parameters to use for the Compact Public Key encryption\n    let cpk_params = tfhe::shortint::parameters::PARAM_PKE_MESSAGE_2_CARRY_2_KS_PBS_TUNIFORM_2M128;\n    // And parameters allowing to keyswitch/cast to the computation parameters.\n    let casting_params = tfhe::shortint::parameters::PARAM_KEYSWITCH_MESSAGE_2_CARRY_2_KS_PBS_TUNIFORM_2M128;\n    // Enable the dedicated parameters on the config\n    let config = tfhe::ConfigBuilder::with_custom_parameters(params)\n        .use_dedicated_compact_public_key_parameters((cpk_params, casting_params)).build();\n\n    // The CRS should be generated in an offline phase then shared to all clients and the server\n    let crs = CompactPkeCrs::from_config(config, 64).unwrap();\n\n    // Then use TFHE-rs as usual\n    let client_key = tfhe::ClientKey::generate(config);\n    let server_key = tfhe::ServerKey::new(&client_key);\n    let public_key = tfhe::CompactPublicKey::try_new(&client_key).unwrap();\n    // This can be left empty, but if provided allows to tie the proof to arbitrary data\n    let metadata = [b'T', b'F', b'H', b'E', b'-', b'r', b's'];\n\n    let clear_a = rng.gen::<u64>();\n    let clear_b = rng.gen::<u64>();\n\n    let proven_compact_list = tfhe::ProvenCompactCiphertextList::builder(&public_key)\n        .push(clear_a)\n        .push(clear_b)\n        .build_with_proof_packed(&crs, &metadata, ZkComputeLoad::Verify)?;\n\n    // Server side\n    let result = {\n        set_server_key(server_key);\n\n        // Verify the ciphertexts\n        let expander =\n            proven_compact_list.verify_and_expand(&crs, &public_key, &metadata)?;\n        let a: tfhe::FheUint64 = expander.get(0)?.unwrap();\n        let b: tfhe::FheUint64 = expander.get(1)?.unwrap();\n\n        a + b\n    };\n\n    // Back on the client side\n    let a_plus_b: u64 = result.decrypt(&client_key);\n    assert_eq!(a_plus_b, clear_a.wrapping_add(clear_b));\n\n    Ok(())\n}
```

--------------------------------

### HPU Device User API

Source: https://github.com/zama-ai/tfhe-rs/blob/main/backends/tfhe-hpu-backend/README.md

Functions for instantiating and initializing the HPU accelerator device. These functions allow users to set up the HPU, configure it from a file, and upload necessary public materials for TFHE computations.

```APIDOC
HpuDevice::new
  - Instantiates the HPU device abstraction.

HpuDevice::from_config
  - Instantiates the HPU device abstraction from a configuration file.

HpuDevice::init
  - Configures and uploads the required public material to the HPU accelerator.
```
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