F2PQNN: a fast and secure two-party inference on quantized convolutional neural networks

Abstract The machine learning as a service (MLaaS) paradigm has been widely adopted across various applications. However, it also raises significant privacy concerns, particularly regarding the exposure of input data and trained models. Two-party computation in convolutional neural network (CNN) inference has emerged as a promising solution to address these privacy issues in MLaaS. Nevertheless, most existing privacy-preserving CNN architectures rely on computationally expensive encryption methods, resulting in prolonged inference times and increased communication overhead. In this paper, we propose F2PQNN, a fast and secure two-party inference framework for quantized CNNs. To minimize reliance on computationally intensive encryption, F2PQNN utilizes two non-colluding servers and integrates secret sharing with oblivious transfer techniques. Furthermore, F2PQNN incorporates quantization techniques, along with batching and asynchronous computation, to significantly accelerate inference predictions. We evaluate the performance of F2PQNN on the MNIST, Fashion-MNIST, CIFAR-10, and STL-10 datasets. Experimental results demonstrate that F2PQNN outperforms existing solutions, achieving a $9.14\times $ speedup and reducing communication overhead by $59.8\times $ on the MNIST dataset.