A Verifiable Discrete Trust Model (VDTM) Using Congruent Federated Learning (CFL) for Social Internet of Vehicles

IF 5.3 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Mohammed Mujib Alshahrani
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引用次数: 0

Abstract

The Social Internet of Vehicles (SIoV) connects cars that are nearby and uses different types of infrastructure to connect people with shared interests. A public, open tool, such as the cloud, is used to share information about things like tolls, traffic, weather, and more. When people share social information, the risks of data leaks and trustworthiness are still not dealt with. This article presents a Verifiable Discrete Trust Model (VDTM) that uses Congruent Federated Learning (CFL) to make social information-sharing tools more trustworthy. The proposed trust model ensures pre- and post-sharing trust verification of the communicating vehicles. Trust is verified as a global identity factor due to the inconsistency between sharing occasions. The CFL is accountable of checking forward and backward trust between the times before and after sharing. In this learning, the congruency is zero-variance detection on both occasions of information sharing. The learning does this check over and over to make sure there is discrete trust in information-sharing times between vehicles, between vehicles and infrastructure, or between vehicles and platforms. The identified trust is valid within the specific interval broadcasted during request initializations. Depending on the trust level, the sharing interval is authenticated using forward and reverse private keys. Therefore, the vehicle's trust results from the maximum information integrity observed in the pre-and post-sharing interval. For the maximum vehicles considered, the proposed model leverages the trust index by 8%, information sharing by 7.15%, and reducing key overhead by 9.35% and time consumption by 7.76%.
使用同义联合学习(CFL)的可验证离散信任模型(VDTM)用于社交车联网
社会车辆互联网(SIoV)将附近的汽车连接起来,并利用不同类型的基础设施将有共同兴趣爱好的人联系起来。云计算等公共开放工具被用来共享收费、交通、天气等信息。当人们分享社交信息时,数据泄露和可信度的风险仍未得到解决。本文提出了一种可验证的离散信任模型(VDTM),该模型采用了同义联合学习(CFL)技术,使社交信息共享工具更加可信。所提出的信任模型可确保对通信工具进行共享前和共享后的信任验证。由于共享场合之间的不一致性,信任是作为全局身份因素进行验证的。CFL 负责检查共享前后的前向和后向信任。在这种学习中,信息共享的两个场合的一致性都是零差异检测。该学习反复进行这种检查,以确保车辆之间、车辆与基础设施之间或车辆与平台之间的信息共享时间存在离散信任。确定的信任在请求初始化期间广播的特定时间间隔内有效。根据信任级别,共享间隔使用正向和反向私钥进行验证。因此,车辆的信任度来自共享前后时间间隔内观察到的最大信息完整性。对于所考虑的最大车辆,所提出的模型利用信任指数提高了 8%,信息共享提高了 7.15%,密钥开销减少了 9.35%,时间消耗减少了 7.76%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
9.60
自引率
0.00%
发文量
25
审稿时长
10 weeks
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