cuSCNN: A Secure and Batch-Processing Framework for Privacy-Preserving Convolutional Neural Network Prediction on GPU - Bai, Yanan; Liu, Quanliang; Wu, Wenyuan; Feng, Yong (Frontiers in Computational Neuroscience, 2021)
The emerging topic of privacy-preserving deep learning as a service has attracted increasing attention in recent years, which focuses on building an efficient and practical neural network prediction framework to secure the client and model-holder data private on the cloud. In such a task, the time cost of performing the secure linear layers is expensive, where matrix multiplication is the atomic operation. Most existing mix-based solutions heavily emphasized employing the BGV-based homomorphic encryption schemes to secure the linear layer on the CPU platform. However, they suffer an efficiency and energy loss when dealing with the larger-scale dataset due to the complicated encoded methods and intractable ciphertext operations. To address it, we propose cuSCNN, a secure and efficient framework to perform the privacy prediction task of convolutional neural network (CNN), which can flexibly perform on the GPU platform. Its main idea is two-fold: 1) To avoid the trivia and complicated homomorphic matrix computations brought by the BGV-based solutions, it adopts the GSW-based homomorphic matrix encryption to efficiently enable the linear layers of CNN, which is a naive method to secure matrix computation operations. 2) To improve the computation efficiency on GPU, a hybrid optimation approach based on CUDA (Compute Unified Device Architecture) has been proposed to improve the parallelism level and memory access speed when performing the matrix multiplication on GPU. Extensive experiments are conducted on industrial datasets and have shown the superior performance of the proposed cuSCNN framework in terms of runtime and power consumption compared to the state-of-the-other frameworks.