B-LNN: Inference-time linear model for secure neural network inference

Abstract

Machine Learning as a Service (MLaaS) provides clients with well-trained neural networks for predicting private data. Conventional prediction processes of MLaaS require clients to send sensitive inputs to the server, or proprietary models must be stored on the client-side device. The former reveals client privacy, while the latter harms the interests of model providers. Existing works on privacy-preserving MLaaS introduce cryptographic primitives to allow two parties to perform neural network inference without revealing either party's data. However, nonlinear activation functions bring high computational overhead and response delays to the inference process of these schemes.In this paper, we analyze the mechanism by which activation functions enhance model expressivity, and design an activation function S-cos that is friendly to secure neural network inference. Our proposed S-cos can be re-parameterized into a linear layer during the inference phase. Further, we propose an inference-time linear model called Beyond Linear Neural Network (B-LNN) equipped with S-cos, which exhibits promising performance on several benchmark datasets.