GPU Accelerated Full Homomorphic Encryption Cryptosystem, Library, and Applications for IoT Systems
Xiaodong Li, Hehe Gao, Jianyi Zhang, Shuya Yang, Xin Jin, Kim‐Kwang Raymond Choo
Abstract
Deep learning, such as convolutional neural networks (CNNs), has been utilized in a number of cloud-based Internet of Things (IoT) applications. Security and privacy are two key considerations in any commercial deployment. Fully homomorphic encryption (FHE) is a popular privacy protection approach, and there have been attempts to integrate FHE with CNNs. However, a simple integration may lead to inefficiency in single-user services and fail to support many of the requirements in real-time applications. In this article, we propose a novel confused modulo projection-based FHE algorithm (CMP-FHE) that is designed to support floating-point operations. Then, we developed a parallelized runtime library based on CMP-FHE and compared it with the widely employed FHE library. Our results show that our library achieves faster speeds. Furthermore, we compared it with the state-of-the-art confused modulo projection-based library and the results demonstrated a speed improvement of 841.67 to 3056.25 times faster. Additionally, we construct a real-time homomorphic CNN (RT-HCNN) under the ciphertext-based framework using CMP-FHE, as well as using graphics processing units (GPUs) to facilitate acceleration. To demonstrate utility, we evaluate the proposed approach on the MNIST data set. Findings demonstrate that our proposed approach achieves a high accuracy rate of 99.13%. Using GPUs acceleration for ciphertext prediction results in us achieving a single prediction time of 79.5 ms. This represents the first homomorphic CNN capable of supporting real-time application and is approximately 58 times faster than Microsoft’s Lola scheme.