用于安全神经网络推理的高效隐私保护方案

陈立全; 杨紫瑄; 张鹏; 马旸

doi:10.1631/FITEE.2400371

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用于安全神经网络推理的高效隐私保护方案

常规文章 | Updated：2025-11-05

- 用于安全神经网络推理的高效隐私保护方案
- Efficient privacy-preserving scheme for secure neural network inference
- 信息与电子工程前沿（英文） 2025年26卷第9期页码：1609-1623
- Affiliations：
  
  1.School of Cyber Science and Engineering, Southeast University, Nanjing 210096, China
  2.Purple Mountain Laboratories, Nanjing 211189, China
- Author bio：
  
  ‡ Corresponding author
- Funds：
  
  National Natural Science Foundation of China(U22B2026;62572121);ZTE Industry University Research Cooperation Project
- DOI：10.1631/FITEE.2400371
  中图分类号： TP391.4
- 收稿：2024-05-08，
  
  修回：2024-11-21，
  
  纸质出版：2025-09
- Accepted：
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陈立全, 杨紫瑄, 张鹏, 等. 用于安全神经网络推理的高效隐私保护方案[J]. 信息与电子工程前沿（英文）, 2025,26(9):1609-1623.

Liquan CHEN, Zixuan YANG, Peng ZHANG, et al. Efficient privacy-preserving scheme for secure neural network inference[J]. Frontiers of Information Technology & Electronic Engineering, 2025, 26(9): 1609-1623.
陈立全, 杨紫瑄, 张鹏, 等. 用于安全神经网络推理的高效隐私保护方案[J]. 信息与电子工程前沿（英文）, 2025,26(9):1609-1623. DOI： 10.1631/FITEE.2400371.

Liquan CHEN, Zixuan YANG, Peng ZHANG, et al. Efficient privacy-preserving scheme for secure neural network inference[J]. Frontiers of Information Technology & Electronic Engineering, 2025, 26(9): 1609-1623. DOI： 10.1631/FITEE.2400371.

摘要

随着智能设备和云服务的广泛应用，加之本地计算与存储资源受限，大量用户倾向于将私有数据传输至云服务器进行处理。然而，敏感数据以明文形式传输引发用户隐私与安全方面的担忧。为应对这些问题，提出一种基于同态加密与安全多方计算的高效隐私保护安全神经网络推理方案，该方案在确保用户与云服务器双方隐私的同时，实现了快速准确的密文推理。首先，将推理过程划分为3个阶段：调整网络结构的合并阶段、执行同态计算的预处理阶段以及隐私数据秘密共享的浮点运算在线阶段。其次，提出一种网络参数合并方法，以降低乘法层级的成本，并减少密文—明文乘法与加法运算次数。最后，提出一种快速卷积算法，以提升计算效率。与其他最先进的方法相比，所提方案在线阶段的线性操作时间至少减少11％，显著降低了推理时间和通信开销。

Abstract

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 efficiency. 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.

关键词

Keywords

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