NssMPClib - Secure Multi-Party Computation Library

Introduction

NssMPClib is a comprehensive Secure Multi-Party Computation (MPC) library developed by Xidian University NSS Lab. It implements privacy-preserving computation protocols based on both Arithmetic Secret Sharing and Function Secret Sharing.

Key Features

• Multiple Security Models: Supports both Semi-Honest and Honest-Majority security assumptions
• Flexible Party Configurations: 2-party and 3-party computation setups
• Multiple Secret Sharing Schemes:
  • Additive Secret Sharing (2-party)
  • Replicated Secret Sharing (3-party)
• Function Secret Sharing (FSS) implementations:
  • Distributed Point Function (DPF)
  • Distributed Comparison Function (DCF)
  • Distributed Interval Containment Function (DICF) with multiple variants (Standard, GROTTO, SIGMA)
• Privacy-Preserving Neural Network Inference: Support for secure model evaluation
• Ring-based Computation: All operations performed on finite rings for cryptographic security

System Requirements

• OS: Linux (required for proper compilation)
• Python: 3.10 or higher (recommended: 3.12)
• PyTorch: >=2.3.0 (recommended: 2.7.1)
• Additional: C++ compiler (gcc/g++), CUDA toolkit (for GPU support)

Installation

Step 1: Clone and Install

git clone https://github.com/XidianNSS/NssMPClib.git
cd NssMPClib
pip install -e .

Step 2: Generate Cryptographic Parameters

Generate essential precomputed parameters for MPC operations:

python scripts/offline_parameter_generation.py

Note: Parameters are saved to ~/NssMPClib/data/ (32-bit in data/32/, 64-bit in data/64/).

Quick Start: 2-Party Computation Example

Server (Party 0) - server.py:

from NssMPC import Party2PC, PartyRuntime, SEMI_HONEST, SecretTensor
import torch

server = Party2PC(0, SEMI_HONEST)
with PartyRuntime(server):
    server.online()
    x = torch.rand([10, 10])
    share_x = SecretTensor(tensor=x)
    result = share_x.restore().convert_to_real_field()
    print("Server result:", result)

Client (Party 1) - client.py:

from NssMPC import Party2PC, PartyRuntime, SEMI_HONEST, SecretTensor

client = Party2PC(1, SEMI_HONEST)
with PartyRuntime(client):
    client.online()
    share_x = SecretTensor(src_id=0)
    result = share_x.restore().convert_to_real_field()
    print("Client result:", result)

Execution:

# Terminal 1: Start server
python server.py

# Terminal 2: Start client (in separate terminal)
python client.py

Running Built-in Examples

1. Arithmetic Secret Sharing (2-Party)

cd tests/primitives/secret_sharing/
# Terminal 1:
python -m unittest test_ass_server.py
# Terminal 2:
python -m unittest test_ass_client.py

2. Neural Network Inference (2-Party)

cd tests/application/neural_network/2pc/
# Terminal 1:
python neural_network_server.py
# Terminal 2:
python neural_network_client.py

3. Replicated Secret Sharing (3-Party)

cd tests/primitives/secret_sharing/
# Terminal 1: python -m unittest test_rss_p0.py
# Terminal 2: python -m unittest test_rss_p1.py  
# Terminal 3: python -m unittest test_rss_p2.py

Configuration

Configure the library in NssMPC/config/configs.json:

{
    "BIT_LEN": 32,           // Ring size: 32 or 64 bits
    "DEVICE": "cuda",        // Compute device: "cpu" or "cuda"
    "DTYPE": "float",        // Data type: "float" or "int"
    "SCALE_BIT": 8,          // Fixed-point scaling bits
    "DEBUG_LEVEL": 2         // Debug level: 0-Secure, 1-Testing, 2-Development
}

DEBUG_LEVEL Details:

• 0 (Secure Mode): Highest security. All pre-generated keys are destroyed after use, strictly following the One-Time Pad principle.
• 1 (Testing Mode): Performance-optimized. Inputs with the same dimensions reuse the same set of keys, facilitating performance testing and batch operations.
• 2 (Development Mode): Convenient for development. Uses a single globally-shared pre-generated key for all operations. ONLY for non-sensitive development environments.

Usage Scenarios:

• DEBUG_LEVEL: 0 - Production environments with real sensitive data
• DEBUG_LEVEL: 1 - Performance testing environments, evaluating performance across different input sizes
• DEBUG_LEVEL: 2 - Protocol development environments, quickly verifying functional correctness

Project Structure

NssMPClib/
├── NssMPC/                   # Main library source
│   ├── application/          # Privacy-preserving applications
│   ├── config/              # Configuration files
│   ├── infra/               # Infrastructure components
│   ├── primitives/          # Cryptographic primitives
│   ├── protocols/           # MPC protocols
│   └── runtime/             # Runtime coordination
├── data/                     # Precomputed cryptographic parameters
├── tests/                   # Test suite and examples
├── tutorials/               # Detailed tutorials
└── scripts/                 # Utility scripts

Precomputed Cryptographic Parameters

The library uses pre-generated parameters for efficiency. Key types include:

Parameter Type	Purpose	Typical Use
AssMulTriples	Multiplication in Arithmetic Secret Sharing	2-party computation
BooleanTriples	AND operations in Boolean Secret Sharing	Secure comparison
RssMulTriples	Multiplication in Replicated Secret Sharing	3-party computation
DICFKey	Distributed Interval Containment Function	Secure comparison
GeLUKey	Gaussian Error Linear Unit activation	Neural networks

and so on…

Tutorials

Detailed tutorials are available in the tutorials/ directory:

Tutorial	Description
Tutorial 0	Library setup and configuration
Tutorial 1	2-party secure computation
Tutorial 2	3-party secure computation
Tutorial 3	Privacy-preserving neural network inference
Tutorial 4	Advanced internal components

Best Practices

1. Separate Processes: Each party must run in separate terminals
2. Use Runtime Context: Always wrap operations in with PartyRuntime(party):
3. Parameter Management: Generate parameters before first use
4. Security Selection: Use DEBUG_LEVEL=0 for production, DEBUG_LEVEL=2 for development

Troubleshooting

Common Issues:

1. “Parameters not found” Error:

   python scripts/offline_parameter_generation.py

2. Port Already in Use: Change base port in configs.json or kill existing processes.

3. CUDA Errors: Set DEVICE: "cpu" in config or check CUDA installation.

Contributing

We welcome contributions! Please:

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Ensure all tests pass
5. Submit a pull request

Citation

If you use NssMPClib in your research, please cite:

@software{nssmpclib,
  title = {NssMPClib: Secure Multi-Party Computation Library},
  author = {Xidian University NSS Lab},
  year = {2024},
  url = {https://github.com/XidianNSS/NssMPClib}
}

License

NssMPClib is released under the MIT License. See the LICENSE file for details.

Contact

• Email: xidiannss@gmail.com
• GitHub: https://github.com/XidianNSS/NssMPClib
• Issues: https://github.com/XidianNSS/NssMPClib/issues

Acknowledgements

Maintained by the Network and System Security (NSS) Laboratory at Xidian University.