xMLP: Revolutionizing Private Inference with Exclusive Square Activation

arXiv:2403.08024v1 Announce Type: new
Abstract: Private Inference (PI) enables deep neural networks (DNNs) to work on private data without leaking sensitive information by exploiting cryptographic primitives such as multi-party computation (MPC) and homomorphic encryption (HE). However, the use of non-linear activations such as ReLU in DNNs can lead to impractically high PI latency in existing PI systems, as ReLU requires the use of costly MPC computations, such as Garbled Circuits. Since square activations can be processed by Beaver's triples hundreds …

abstract arxiv computation cs.cr cs.lg data encryption exclusive homomorphic encryption however inference information linear mpc networks neural networks non-linear private data relu square type work