网络与信息安全学报 ›› 2020, Vol. 6 ›› Issue (4): 130-139.doi: 10.11959/j.issn.2096-109x.2020050

• 学术论文 • 上一篇    

面向深度神经网络的安全计算协议设计方法

毕仁万1,陈前昕1,熊金波1(),刘西蒙2   

  1. 1 福建师范大学数学与信息学院,福建 福州 350117
    2 福州大学数学与计算机科学学院,福建 福州 350108
  • 修回日期:2020-07-03 出版日期:2020-08-01 发布日期:2020-08-13
  • 作者简介:毕仁万(1996- ),男,湖南常德人,福建师范大学硕士生,主要研究方向为安全深度学习、安全多方计算|陈前昕(1996- ),男,福建泉州人,福建师范大学硕士生,主要研究方向为安全深度学习、隐私保护技术|熊金波(1981- ),男,湖南益阳人,博士,福建师范大学教授,主要研究方向为安全深度学习、移动群智感知、隐私保护技术|刘西蒙(1988- ),男,陕西西安人,博士,福州大学教授,主要研究方向为云安全、应用密码学、大数据安全
  • 基金资助:
    国家自然科学基金(61872088);国家自然科学基金(U1804263);国家自然科学基金(61702105);国家自然科学基金(61872090);福建省自然科学基金资助项目(2019J01276);贵州省公共大数据重点实验室开放课题(2019BDKFJJ004)

Design method of secure computing protocol for deep neural network

Renwan BI1,Qianxin CHEN1,Jinbo XIONG1(),Ximeng LIU2   

  1. 1 College of Mathematics and Informatics,Fujian Normal University,Fuzhou 350117,China
    2 College of Mathematics and computer science,Fuzhou University,Fuzhou 350108,China
  • Revised:2020-07-03 Online:2020-08-01 Published:2020-08-13
  • Supported by:
    The National Natural Science Foundation of China(61872088);The National Natural Science Foundation of China(U1804263);The National Natural Science Foundation of China(61702105);The National Natural Science Foundation of China(61872090);The Natural Science Foundation of Fujian Province,China(2019J01276);The Guizhou Provincial Key Laboratory of Public Big Data Research Fund(2019BDKFJJ004)

摘要:

针对深度神经网络模型计算过程中存在的信息泄露问题,结合加性秘密共享方案,在两台非共谋的边缘服务器间设计安全高效的交互计算协议。考虑到非线性函数不能直接拆分,首先提出一组基本转换协议,实现加性副本和乘性副本的安全转换,经过少量调用,可以安全计算幂函数、比较、指数、对数、除法等底层函数。由于数据传递和计算特点,协议可以扩展至数组计算。理论分析证明了协议的正确性、高效性和安全性,实验结果表明,协议具有较小的误差,其计算和通信开销均优于现有设计方案。

关键词: 深度神经网络, 加性秘密共享, 安全计算协议, 加法-乘法转换, 数组单元

Abstract:

Aiming at the information leakage problem in the process of deep neural network model calculation,a series of secure and efficient interactive computing protocols were designed between two non-collusive edge servers in combination with the additive secret sharing scheme.Since the nonlinear function cannot be split directly,a set of basic conversion protocols were proposed to realize the secure conversion of additive and multiplicative shares.After a few invokes,the power,comparison,exponential,logarithm,division and other low-level functions can be calculated securely.Due to the characteristics of data transfer and computation,the proposed protocols can be extended to array computation.Theoretical analysis ensures the correctness,efficiency and security of these protocols.The experimental results show that the error of these protocols is negligible,and the computational costs and communication overhead are better than the existing schemes.

Key words: deep neural network, additive secret sharing, secure computing protocol, add-multiply transformation, array unit

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