基于智能交通的隐私保护道路状态实时监测方案

李家印; 郭文忠; 李小燕; 刘西蒙

doi:10.11959/j.issn.1000-436x.2020110

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基于智能交通的隐私保护道路状态实时监测方案

学术论文 | 更新时间：2024-06-05

- 基于智能交通的隐私保护道路状态实时监测方案
- Privacy-preserving real-time road conditions monitoring scheme based on intelligent traffic
- 通信学报 2020年41卷第7期页码：73-83
- 作者机构：
  
  1. 福州大学数学与计算机科学学院，福建福州 350108
  2. 福州大学网络安全福建省高校重点实验室，福建福州 350108
  3. 福州大学福建省网络计算与智能信息处理重点实验室，福建福州 350108
- 作者简介：
  
  [ "李家印（1990– ），男，山东济宁人，福州大学博士生，主要研究方向为移动数据采集、智慧城市、云外包数据存储、隐私保护、密文计算等" ]
  [ "郭文忠（1979– ），男，福建惠安人，博士，福州大学教授、博士生导师，主要研究方向为计算机智能及其在计算机网络中的应用等" ]
  [ "李小燕（1988- ），女，福建福州人，博士，福州大学讲师，主要研究方向为数据中心网络、网络安全、算法的设计与分析等" ]
  [ "刘西蒙（1988– ），男，陕西西安人，博士，福州大学研究员，主要研究方向为隐私计算、密文数据挖掘、大数据隐私保护、可搜索加密等" ]
- 基金信息：
  
  国家自然科学基金资助项目(61702105);国家自然科学基金资助项目(U1804263);国家自然科学基金资助项目(61672159);国家自然科学基金资助项目(U1705262)
- DOI：10.11959/j.issn.1000-436x.2020110
  中图分类号： TP302
- 网络出版日期：2020-07，
  
  纸质出版日期：2020-07-25
- 稿件说明：
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李家印, 郭文忠, 李小燕, 等. 基于智能交通的隐私保护道路状态实时监测方案[J]. 通信学报, 2020,41(7):73-83.

Jiayin LI, Wenzhong GUO, Xiaoyan LI, et al. Privacy-preserving real-time road conditions monitoring scheme based on intelligent traffic[J]. Journal on communications, 2020, 41(7): 73-83.
李家印, 郭文忠, 李小燕, 等. 基于智能交通的隐私保护道路状态实时监测方案[J]. 通信学报, 2020,41(7):73-83. DOI： 10.11959/j.issn.1000-436x.2020110.

Jiayin LI, Wenzhong GUO, Xiaoyan LI, et al. Privacy-preserving real-time road conditions monitoring scheme based on intelligent traffic[J]. Journal on communications, 2020, 41(7): 73-83. DOI： 10.11959/j.issn.1000-436x.2020110.

摘要

为缓解道路的交通压力，减少道路拥堵现象的出现及避免交通事故的发生，结合安全、K最近邻（KNN）算法，提出了一种基于智能交通的隐私保护道路拥堵状态的实时监测（PPIM）算法。为了确保交通数据的安全，采用安全多方计算策略将数据内容随机分成独立的部分，通过不共谋的多服务器对数据分量进行存储和加密。为了提升道路状态监测的精度，提出了一种改进型的 KNN 交通监测算法，借助数据的相似度计算，获取衡量道路之间交通状态关系程度的相关值，并将其作为权重系数与传统的 KNN 算法进行整合。为加快密态数据的处理速度，设计了一系列的数据安全计算协议，实现了数据的安全处理。另外，利用真实的交通数据对该算法进行验证，实验结果表明改进型 KNN 算法有助于提高道路监测的准确度。实验分析表明，所提算法在保证数据的安全同时可以提高交通监测的精度。

Abstract

To alleviate the traffic pressure on roads

reduce the appearance of road congestion

and avoid the occurrence of traffic accidents

a privacy-preserving intelligent monitoring (PPIM) scheme based on intelligent traffic was proposed in combination with the safe and k-nearest neighbor (KNN) algorithm.To ensure the security of traffic data

the data content was randomly divided into independent parts via the secure multi-party computing strategy

and the data components were stored and encrypted separately by non-colluding multi-servers.To improve the accuracy of road condition monitoring

an improved KNN traffic monitoring algorithm was proposed.By virtue of the similarity calculation of data

the correlation value to measure the degree of traffic condition relationship between roads was obtained.And it was integrated with the KNN as the weight coefficient.To speed up the processing of dense data

a series of data security computing protocols were designed

and the data security processing was realized.In addition

real traffic data were used to verify the algorithm.The results show that the improved KNN algorithm is helpful to improve the accuracy of traffic monitoring.The analysis shows that the algorithm can not only guarantee the safety of data but improve the accuracy of traffic monitoring.

关键词

Keywords

references

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