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.