PolyMPCNet: Towards ReLU-free Neural Architecture Search in Two-party Computation Based Private Inference. (arXiv:2209.09424v1 [cs.CR])

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 …

architecture computation free party search