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。
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.
作者
李家印
郭文忠
李小燕
刘西蒙
LI Jiayin;GUO Wenzhong;LI Xiaoyan;LIU Ximeng(College of Mathematics and Computer Science,Fuzhou University,Fuzhou 350108,China;Key Lab of Information Security of Network Systems,Fuzhou University,Fuzhou 350108,China;Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing,Fuzhou University,Fuzhou 350108,China)
出处
《通信学报》
EI
CSCD
北大核心
2020年第7期73-83,共11页
Journal on Communications
基金
国家自然科学基金资助项目(No.61702105,No.U1804263,No.61672159,No.U1705262)。
关键词
智能交通
隐私保护
空间距离
K最近邻
intelligent traffic
privacy-preserving
space distance
KNN
