This repository contains the source code for the paper "Is MPC Secure? Leveraging Neural Network Classifiers to Detect Data Leakage Vulnerabilities in MPC Implementations", which has been accepted for publication at IEEE Symposium on Security and Privacy (S&P) 2025.
MPCGuard is a practical framework designed to detect data leakage vulnerabilities in multi-party computation (MPC) implementations. With the increasing adoption of MPC for privacy-preserving computations, ensuring their security against data leakage vulnerabilities is critical. Unlike traditional security analysis methods that focus on theoretical proofs, MPCGuard employs neural network classifiers to analyze real-world MPC implementations and identify potential vulnerabilities.
- Automated Data Leakage Detection: Utilizes neural network classifiers designed according to MPC characteristics to identify potential data leakage vulnerabilities.
- Delta-Based Vulnerability Localization: Efficiently pinpoints the source of leakage within the code.
- Python 3.10+
- PyTorch
- CUDA 12.4 (for GPU acceleration, optional)
- NVIDIA GeForce RTX 3080 (or any CUDA-compatible GPU, optional)
coming soon...
coming soon...MPCGuard currently supports:
- Crypten
- TF-Encrypted
- MP-SPDZ
You can test specific MPC protocols by specifying the protocol name. Supported protocols include:
mul(Multiplication)linear(Linear Combination)ltz(Less Than Zero)eqz(Equal to Zero)truncpr(Probabilistic Truncation)
This project is licensed under the MIT License. See the LICENSE file for details.
For any questions or contributions, please open an issue or submit a pull request!