ABNN 2

Abstract

Data privacy and security issues are preventing a lot of potential on-cloud machine learning as services from happening. In the recent past, secure multi-party computation (MPC) has been used to achieve the secure neural network predictions, guaranteeing the privacy of data. However, the cost of the existing two-party solutions is expensive and they are impractical in real-world setting. In this work, we utilize the advantages of quantized neural network (QNN) and MPC to present ABNN2, a practical secure two-party framework that can realize arbitrary-bitwidth quantized neural network predictions. Concretely, we propose an efficient and novel matrix multiplication protocol based on 1-out-of-N OT extension and optimize the the protocol through a parallel scheme. In addition, we design optimized protocol for the ReLU function. The experiments demonstrate that our protocols are about 2X-36X and 1.4X--7X faster than SecureML (S&P'17) and MiniONN (CCS'17) respectively. And ABNN2 obtain comparable efficiency as state of the art QNN prediction protocol QUOTIENT (CCS'19), but the later only supports ternary neural network.