Optimism-geth with NTT Precompiles (liboqs Implementation)

Fork of op-geth with precompiled contracts for Number Theoretic Transform (NTT) operations using open-quantum-safe liboqs via CGO bindings, enabling efficient on-chain post-quantum cryptographic operations.

Precompiled Contracts

Supported Schemes

Installation

1. Install liboqs with NTT CGO Bindings

# Clone and install dependencies
git clone -b feature/ntt-cgo-bindings https://github.com/yhl125/liboqs.git
sudo apt install astyle cmake gcc ninja-build libssl-dev python3-pytest python3-pytest-xdist unzip xsltproc doxygen graphviz python3-yaml valgrind pkg-config

# Build liboqs and Go bindings
cd liboqs/bindings/go
make all

2. Configure Environment

# Set paths (adjust to your installation directory)
export PKG_CONFIG_PATH=/path/to/liboqs/bindings/go/.config:$PKG_CONFIG_PATH
export DYLD_LIBRARY_PATH=/path/to/liboqs/build/lib:$DYLD_LIBRARY_PATH  # macOS
export LD_LIBRARY_PATH=/path/to/liboqs/build/lib:$LD_LIBRARY_PATH      # Linux

3. Build op-geth

make geth

API Reference

NTT_FW (0x12) - Forward Transform

Transforms coefficients into NTT domain using liboqs.

Input:

[0:4]   ring_degree (uint32, big-endian)
[4:12]  modulus (uint64, big-endian)
[12:*]  coefficients (Falcon: uint16×N, ML-DSA: int32×N, big-endian)

Output: NTT-transformed coefficients (same format as input)

Gas Cost:

• Falcon-512: 500 gas (~9.4μs, 53 mgas/s)
• Falcon-1024: 1,080 gas (~20.4μs, 53 mgas/s)
• ML-DSA: 256 gas (~4.8μs, 53 mgas/s)

NTT_INV (0x13) - Inverse Transform

Transforms NTT domain coefficients back to standard representation.

Input: Same as NTT_FW (coefficients in NTT domain)

Output: Coefficients in standard representation

Gas Cost:

• Falcon-512: 500 gas (~9.4μs, 53 mgas/s)
• Falcon-1024: 1,080 gas (~20.3μs, 53 mgas/s)
• ML-DSA: 340 gas (~6.4μs, 53 mgas/s)

VECMULMOD (0x14) - Vector Multiplication

Element-wise modular multiplication in NTT domain.

Input:

[0:4]   ring_degree (uint32, big-endian)
[4:12]  modulus (uint64, big-endian)
[12:12+n*size] vector_a
[12+n*size:*]  vector_b

Output: Element-wise product (a[i] * b[i]) mod q

Gas Cost: ceil(0.32 × n)

• Falcon-512: 164 gas (~2.9μs, 56 mgas/s)
• Falcon-1024: 328 gas (~6.0μs, 55 mgas/s)
• ML-DSA: 82 gas (~2.0μs, 42 mgas/s)

VECADDMOD (0x15) - Vector Addition

Element-wise modular addition.

Input: Same as VECMULMOD

Output: Element-wise sum (a[i] + b[i]) mod q

Gas Cost: ceil(0.3 × n)

• Falcon-512: 154 gas (~2.8μs, 55 mgas/s)
• Falcon-1024: 308 gas (~5.8μs, 53 mgas/s)
• ML-DSA: 77 gas (~1.6μs, 47 mgas/s)

Testing

cd core/vm

# All NTT tests
go test -v -run TestPrecompiled.*NTT

# Specific tests
go test -v -run TestPrecompiledNTT_FW
go test -v -run TestPrecompiledNTT_VECMULMOD

# Benchmarks
go test -bench=BenchmarkPrecompiledNTT -benchtime=5s

Test Coverage:

• Scheme detection (Falcon-512/1024, ML-DSA)
• Input validation (malformed inputs, invalid parameters)
• Round-trip verification (INTT(NTT(x)) = x)
• Cross-scheme isolation
• Performance benchmarks with crypto standards

Benchmark Results

Benchmarks were run on an Intel(R) Xeon(R) CPU @ 2.20GHz. For detailed results, please see the files below:

• Ecrecover Benchmark Test Results
• NTT & NTT Vector Operations Benchmark Test Results