The increasing adoption of smart devices and cloud services,coupled with limitations in local computing and storage resources,prompts numerous users to transmit private data to cloud servers for processing.However,the...The increasing adoption of smart devices and cloud services,coupled with limitations in local computing and storage resources,prompts numerous users to transmit private data to cloud servers for processing.However,the transmission of sensitive data in plaintext form raises concerns regarding users'privacy and security.To address these concerns,this study proposes an efficient privacy-preserving secure neural network inference scheme based on homomorphic encryption and secure multi-party computation,which ensures the privacy of both the user and the cloud server while enabling fast and accurate ciphertext inference.First,we divide the inference process into three stages,including the merging stage for adjusting the network structure,the preprocessing stage for performing homomorphic computations,and the online stage for floating-point operations on the secret sharing of private data.Second,we propose an approach of merging network parameters,thereby reducing the cost of multiplication levels and decreasing both ciphertext-plaintext multiplication and addition operations.Finally,we propose a fast convolution algorithm to enhance computational eficiency.Compared with other state-of-the-art methods,our scheme reduces the linear operation time in the online stage by at least 11%,significantly reducing inference time and communication overhead.展开更多
基金Project supported by the National Natural Science Foundation of China(No.U22B2026 and 62572121)the ZTE Industry University Research Cooperation Project。
文摘The increasing adoption of smart devices and cloud services,coupled with limitations in local computing and storage resources,prompts numerous users to transmit private data to cloud servers for processing.However,the transmission of sensitive data in plaintext form raises concerns regarding users'privacy and security.To address these concerns,this study proposes an efficient privacy-preserving secure neural network inference scheme based on homomorphic encryption and secure multi-party computation,which ensures the privacy of both the user and the cloud server while enabling fast and accurate ciphertext inference.First,we divide the inference process into three stages,including the merging stage for adjusting the network structure,the preprocessing stage for performing homomorphic computations,and the online stage for floating-point operations on the secret sharing of private data.Second,we propose an approach of merging network parameters,thereby reducing the cost of multiplication levels and decreasing both ciphertext-plaintext multiplication and addition operations.Finally,we propose a fast convolution algorithm to enhance computational eficiency.Compared with other state-of-the-art methods,our scheme reduces the linear operation time in the online stage by at least 11%,significantly reducing inference time and communication overhead.
