Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy challenges.One key requirement for such systems is the preservation of user privacy,ensuring a seamless experie...Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy challenges.One key requirement for such systems is the preservation of user privacy,ensuring a seamless experience in driving,navigation,and communication.These privacy needs are influenced by various factors,such as data collected at different intervals,trip durations,and user interactions.To address this,the paper proposes a Support Vector Machine(SVM)model designed to process large amounts of aggregated data and recommend privacy preserving measures.The model analyzes data based on user demands and interactions with service providers or neighboring infrastructure.It aims to minimize privacy risks while ensuring service continuity and sustainability.The SVMmodel helps validate the system’s reliability by creating a hyperplane that distinguishes between maximum and minimum privacy recommendations.The results demonstrate the effectiveness of the proposed SVM model in enhancing both privacy and service performance.展开更多
针对煤与瓦斯突出事故的复杂性以及数据获取困难导致预测准确率低的问题,提出基于密度的噪声应用空间聚类-改进哈里斯鹰优化-支持向量机(density based spatial clustering of applications with noise-improved Harris hawks optimizat...针对煤与瓦斯突出事故的复杂性以及数据获取困难导致预测准确率低的问题,提出基于密度的噪声应用空间聚类-改进哈里斯鹰优化-支持向量机(density based spatial clustering of applications with noise-improved Harris hawks optimization-support vector machine, DBSCAN-IHHO-SVM)预测模型。首先,选取瓦斯含量、瓦斯压力、煤层孔隙率、煤层坚固性系数作为预测指标,对数据中的缺失值采用均值填补处理,利用生成式对抗网络(generative adversarial network, GAN)扩充突出数据量。接着,采用DBSCAN从非突出数据中识别潜在危险数据,并将其作为新的突出数据。最后,引入IHHO调整SVM模型参数,将处理后的数据输入IHHO-SVM模型进行预测分析。结果表明,相比于原始SVM模型,DBSCAN-IHHO-SVM模型的整体预测准确率、危险数据识别率分别提升了5.87%、38.46%。在突出数据样本有限的情况下,DBSCAN-IHHO-SVM模型能有效挖掘非突出数据潜在信息,实现精准预警,为该领域研究提供了新思路。展开更多
基金supported by the Deanship of Graduate Studies and Scientific Research at University of Bisha for funding this research through the promising program under grant number(UB-Promising-33-1445).
文摘Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy challenges.One key requirement for such systems is the preservation of user privacy,ensuring a seamless experience in driving,navigation,and communication.These privacy needs are influenced by various factors,such as data collected at different intervals,trip durations,and user interactions.To address this,the paper proposes a Support Vector Machine(SVM)model designed to process large amounts of aggregated data and recommend privacy preserving measures.The model analyzes data based on user demands and interactions with service providers or neighboring infrastructure.It aims to minimize privacy risks while ensuring service continuity and sustainability.The SVMmodel helps validate the system’s reliability by creating a hyperplane that distinguishes between maximum and minimum privacy recommendations.The results demonstrate the effectiveness of the proposed SVM model in enhancing both privacy and service performance.
