Privacy-preserving real-time road conditions monitoring scheme based on intelligent traffic
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...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2020-07-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2020110/ |
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| _version_ | 1841539310697840640 |
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| author | Jiayin LI Wenzhong GUO Xiaoyan LI Ximeng LIU |
| author_facet | Jiayin LI Wenzhong GUO Xiaoyan LI Ximeng LIU |
| author_sort | Jiayin LI |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c983b3ecb25a46b6a2965aac5937b5f0 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2020-07-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-c983b3ecb25a46b6a2965aac5937b5f02025-01-14T07:19:39ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2020-07-0141738359736572Privacy-preserving real-time road conditions monitoring scheme based on intelligent trafficJiayin LIWenzhong GUOXiaoyan LIXimeng LIUTo 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.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2020110/intelligent trafficprivacy-preservingspace distanceKNN |
| spellingShingle | Jiayin LI Wenzhong GUO Xiaoyan LI Ximeng LIU Privacy-preserving real-time road conditions monitoring scheme based on intelligent traffic Tongxin xuebao intelligent traffic privacy-preserving space distance KNN |
| title | Privacy-preserving real-time road conditions monitoring scheme based on intelligent traffic |
| title_full | Privacy-preserving real-time road conditions monitoring scheme based on intelligent traffic |
| title_fullStr | Privacy-preserving real-time road conditions monitoring scheme based on intelligent traffic |
| title_full_unstemmed | Privacy-preserving real-time road conditions monitoring scheme based on intelligent traffic |
| title_short | Privacy-preserving real-time road conditions monitoring scheme based on intelligent traffic |
| title_sort | privacy preserving real time road conditions monitoring scheme based on intelligent traffic |
| topic | intelligent traffic privacy-preserving space distance KNN |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2020110/ |
| work_keys_str_mv | AT jiayinli privacypreservingrealtimeroadconditionsmonitoringschemebasedonintelligenttraffic AT wenzhongguo privacypreservingrealtimeroadconditionsmonitoringschemebasedonintelligenttraffic AT xiaoyanli privacypreservingrealtimeroadconditionsmonitoringschemebasedonintelligenttraffic AT ximengliu privacypreservingrealtimeroadconditionsmonitoringschemebasedonintelligenttraffic |