Secure CNN Training and Inference based on Multi-key Fully Homomorphic Encryption
Hong Qin, Debiao He, Qi Feng, Min Luo · 2023
Convolutional neural network (CNN) has attracted increasing attention and been widely used in imaging processing, bioinformatics and so on. As the cloud computing and multiparty computing are booming, the training and inference data of convolutional neural network often comes from diverse users. These users tend to jointly perform the computation but reluctantly share original data with others. Multi-key fully homomorphic encryption (MKFHE) supports homomorphic computation on ciphertexts encrypted with different keys, which is especially suitable for this scenario. In this paper, we firstly propose secure convolution, matrix multiplication, comparison and maximum protocols based on MKFHE. Then we design the secure CNN training and inference framework, outsourcing almost all computations to cloud server. To improve the efficiency, we use key switching technique for ciphertext transformation. We prove that the proposed frameworks are secure and feasible. The theoretical and experimental analysis show that our framework achieves the trade-off between security, efficiency and scalability.