面向车联网数据隐私保护的高效分布式模型共享策略

莫梓嘉; 高志鹏; 杨杨; 林怡静; 孙山; 赵晨

doi:10.11959/j.issn.1000-436x.2022074

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面向车联网数据隐私保护的高效分布式模型共享策略

学术论文 | 更新时间：2024-06-05

- 面向车联网数据隐私保护的高效分布式模型共享策略
- Efficient distributed model sharing strategy for data privacy protection in Internet of vehicles
- 通信学报 2022年43卷第4期页码：83-94
- 作者机构：
  
  北京邮电大学网络与交换技术国家重点实验室，北京 100876
- 作者简介：
  
  [ "莫梓嘉（1996- ），女，河北保定人，北京邮电大学博士生，主要研究方向为边缘智能、模型轻量化等" ]
  [ "高志鹏（1980- ），男，山东滨州人，博士，北京邮电大学教授、博士生导师，主要研究方向为云计算、网络服务与管理、边缘计算等" ]
  [ "杨杨（1981- ），女，山东淄博人，博士，北京邮电大学副教授、博士生导师，主要研究方向为数据挖掘、人工智能等" ]
  [ "林怡静（1997– ），女，福建莆田人，北京邮电大学博士生，主要研究方向为边缘计算、区块链等" ]
  [ "孙山（1998– ），男，山东济宁人，北京邮电大学硕士生，主要研究方向为边缘智能、云边协同等" ]
  [ "赵晨（1992– ），男，河南南阳人，北京邮电大学博士生，主要研究方向为边缘计算、联邦学习等" ]
- 基金信息：
  
  国家自然科学基金资助项目(62072049)
- DOI：10.11959/j.issn.1000-436x.2022074
  中图分类号： TP393
- 网络出版日期：2022-04，
  
  纸质出版日期：2022-04-25
- 稿件说明：
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莫梓嘉, 高志鹏, 杨杨, 等. 面向车联网数据隐私保护的高效分布式模型共享策略[J]. 通信学报, 2022,43(4):83-94.

Zijia MO, Zhipeng GAO, Yang YANG, et al. Efficient distributed model sharing strategy for data privacy protection in Internet of vehicles[J]. Journal on communications, 2022, 43(4): 83-94.
莫梓嘉, 高志鹏, 杨杨, 等. 面向车联网数据隐私保护的高效分布式模型共享策略[J]. 通信学报, 2022,43(4):83-94. DOI： 10.11959/j.issn.1000-436x.2022074.

Zijia MO, Zhipeng GAO, Yang YANG, et al. Efficient distributed model sharing strategy for data privacy protection in Internet of vehicles[J]. Journal on communications, 2022, 43(4): 83-94. DOI： 10.11959/j.issn.1000-436x.2022074.

摘要

针对车联网隐私数据共享面临的效率问题，提出了基于区块链的高效分布式模型共享策略。针对车联网场景下多实体、多角色的数据共享需求，通过在车辆、路边单元和基站之间构建主从链架构，实现了分布式模型安全共享；提出了基于激励机制的异步联邦学习算法，以激励车辆及路边单元参与优化过程；构造了混合 PBFT的改进DPoS共识算法来降低通信成本、提高共识效率。实验分析表明，所提机制能够提高数据共享效率，并具有一定的可扩展性。

Abstract

Aiming at the efficiency problem of privacy data sharing in the Internet of vehicles (IoV)

an efficient distributed model sharing strategy based on blockchain was proposed.In response to the data sharing requirements among multiple entities and roles in the IoV

a master-slave chain architecture was built between vehicles

roadside units

and base stations to achieve secure sharing of distributed models.An asynchronous federated learning algorithm based on motivate mechanism was proposed to encourage vehicles and roadside units to participate in the optimization process.An improved DPoS consensus algorithm with hybrid PBFT was constructed to reduce communication costs and improve consensus efficiency.Experimental analysis shows that the proposed mechanism can improve the efficiency of data sharing and has certain scalability.

关键词

Keywords

references

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