MPCDiff: Testing and Repairing MPC-Hardened Deep Learning Models
Qi Pang
Network and Distributed System Security (NDSS) Symposium 2024 · Day 2 · Privacy & Fingerprinting
Secure Multi-Party Computation (MPC) has emerged as a foundational technology for privacy-preserving machine learning, enabling multiple entities to collaboratively execute computations on sensitive data and pre-trained models without compromising private information. Major industry players like Meta, Microsoft, and Alibaba have invested heavily in developing sophisticated MPC frameworks built upon popular deep learning libraries such as TensorFlow and PyTorch. While these frameworks streamline the integration of complex deep neural network (DNN) operators with MPC primitives, a critical gap has remained: a principled and systematic methodology for understanding and ensuring the correctness and quality of these MPC implementations.