PolyMPCNet: Towards ReLU-free Neural Architecture Search in Two-party Computation Based Private Inference

Understand

The rapid growth and deployment of deep learning (DL) has witnessed emerging privacy and security concerns.

• To mitigate these issues, secure multi-party computation (MPC) has been discussed, to enable the privacy-preserving DL computation.
• In practice, they often come at very high computation and communication overhead, and potentially prohibit their popularity in large scale systems.
• Two orthogonal research trends have attracted enormous interests in addressing the energy efficiency in secure deep learning, i.e., overhead reduction of MPC comparison protocol, and hardware acceleration.