> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/octra-labs/pvac_hfhe_cpp/llms.txt
> Use this file to discover all available pages before exploring further.
# Introduction
> A proof-of-concept implementation of PVAC-HFHE, a fully homomorphic encryption scheme based on Learning with Parity and Noise over a 127-bit prime field
## What is PVAC-HFHE?
PVAC-HFHE is a fully homomorphic encryption (FHE) library that enables computation on encrypted data without decryption. Built on the assumption of binary parity for learning with noise and arithmetic operations over a 127-bit prime field, it provides a practical implementation for privacy-preserving computation.
This is a proof-of-concept implementation from early 2024, based on research from the Moscow Institute of Physics and Technology (MIPT) on threshold behavior and fractional colorability of random hypergraphs.
## Key features
Perform addition, subtraction, and multiplication on encrypted values without exposing plaintext data.
Operations over a large prime field provide strong security guarantees while maintaining efficiency.
Uses a dense random k-uniform hypergraph for efficient cryptographic operations.
Simple integration with just `#include ` and C++17 compiler support.
## Quick example
Here's a taste of what you can do with PVAC-HFHE:
```cpp theme={null}
#include
using namespace pvac;
// Generate keys
Params prm;
PubKey pk;
SecKey sk;
keygen(prm, pk, sk);
// Encrypt values
Cipher a = enc_value(pk, sk, 42);
Cipher b = enc_value(pk, sk, 17);
// Compute on encrypted data
Cipher sum = ct_add(pk, a, b); // 42 + 17 = 59
Cipher prod = ct_mul(pk, a, b); // 42 * 17 = 714
// Decrypt results
auto result_sum = dec_value(pk, sk, sum); // 59
auto result_prod = dec_value(pk, sk, prod); // 714
```
## Use cases
Train and evaluate ML models on encrypted data, enabling secure data collaboration without exposing sensitive information.
Offload computation to untrusted cloud servers while keeping your data encrypted end-to-end.
Perform statistical analysis and aggregations on encrypted datasets without revealing individual values.
Execute queries on encrypted databases, maintaining data privacy even during computation.
## Get started
Set up PVAC-HFHE in your C++17 project in minutes.
Build your first homomorphic encryption application.
Understand the cryptographic foundations of PVAC-HFHE.
Explore the complete API documentation.
## Requirements
PVAC-HFHE has minimal dependencies:
* **C++ Standard**: C++17 or later
* **Compiler**: GCC 9+, Clang 10+, or MSVC 2019+
* **CPU**: x86-64 architecture with PCLMUL support (recommended for optimal performance)
PCLMUL (carry-less multiplication) instructions significantly improve cryptographic operation performance. Most modern x86-64 processors support this feature.