网络与信息安全学报 ›› 2021, Vol. 7 ›› Issue (6): 99-112.doi: 10.11959/j.issn.2096-109x.2021083

• 专栏Ⅱ:区块链技术 • 上一篇    

基于信誉评估机制和区块链的移动网络联邦学习方案

杨明1, 胡学先1, 张启慧1, 魏江宏1, 刘文芬2   

  1. 1 信息工程大学,河南 郑州 450001
    2 桂林电子科技大学,广西 桂林541004
  • 修回日期:2021-03-30 出版日期:2021-12-01 发布日期:2021-12-01
  • 作者简介:杨明(1994− ),男,内蒙古包头人,信息工程大学硕士生,主要研究方向为大数据安全、区块链技术
    胡学先(1982− ),男,湖北红安人,博士,信息工程大学副教授,主要研究方向为安全协议、大数据安全
    张启慧(1983− ),女,安徽阜阳人,博士,信息工程大学讲师,主要研究方向为安全协议、大数据安全
    魏江宏(1987− ),男,甘肃定西人,博士,信息工程大学讲师,主要研究方向为属性基密码、大数据安全
    刘文芬(1965− ),女,湖北安陆人,桂林电子科技大学教授、博士生导师,主要研究方向为网络通信、信息安全
  • 基金资助:
    国家自然科学基金(62172433);国家自然科学基金(62172434);国家自然科学基金(61862011);国家自然科学基金(61872449);广西密码学与信息安全重点实验室研究课题(GCIS201704)

Federated learning scheme for mobile network based on reputation evaluation mechanism and blockchain

Ming YANG1, Xuexian HU1, Qihui ZHANG1, Jianghong WEI1, Wenfen LIU2   

  1. 1 Strategic Support Force Information Engineering University, Zhengzhou 450001, China
    2 Guilin University of Electronic Technology, Guilin 541004, China
  • Revised:2021-03-30 Online:2021-12-01 Published:2021-12-01
  • Supported by:
    TheNational Natural Science Foundation of China(62172433);TheNational Natural Science Foundation of China(62172434);TheNational Natural Science Foundation of China(61862011);TheNational Natural Science Foundation of China(61872449);Guangxi Key Laboratory of Cryptography and Information Security(GCIS201704)

摘要:

联邦学习是一种新兴的分布式机器学习技术,通过将训练任务下放到用户端,仅将训练得到的模型参数发送给服务端,整个过程并不需要参与方直接共享数据,从而很大限度上规避了隐私问题。然而,这种学习模式中移动用户间没有预先建立信任关系,用户之间进行合作训练时会存在安全隐患。针对上述问题,提出一种基于信誉评估机制和区块链的移动网络联邦学习方案,该方案允许服务端利用主观逻辑模型对参与训练的移动用户进行信誉评估,并且基于区块链智能合约技术为其提供可信的信誉意见共享环境和动态访问策略接口。理论和实验分析结果表明,此方案可以使服务端选择可靠的用户进行训练,同时能够实现更公平和有效的信誉计算,提高联邦学习的准确性。

关键词: 移动网络, 联邦学习, 区块链, 信誉管理

Abstract:

Federated learning is a new distributed machine learning technology, where training tasks are deployed on user side and training model parameters are sent to the server side.In the whole process, participants do not need to share their own data directly, which greatly avoids privacy issues.However, the trust relationship between mobile users in the learning model has not been established in advance, there is hidden safety when users perform cooperative train with each other.In view of the above problems, a federated learning scheme for mobile network based on reputation evaluation mechanism and blockchain was proposed.The scheme allowed the server side to use subjective logic models to evaluate the reputation of participating mobile users and provided them with credible reputation opinions sharing environment and dynamic access strategy interface based on the technique of smart contract of blockchain.Theoretical and experimental analys is results show that the scheme can enable the server side to select reliable users for training.And it can achieve more fair and effective reputation calculations, which improves the accuracy of federated learning.

Key words: mobile network, federated learning, blockchain, reputation management

中图分类号: 

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