ارزیابی حریم خصوصی برای شبکه های نقلیه ای مبتنی بر ابر
ترجمه نشده

ارزیابی حریم خصوصی برای شبکه های نقلیه ای مبتنی بر ابر

عنوان فارسی مقاله: روش ارزیابی حریم خصوصی با توجه به عدم قطعیت برای شبکه های نقلیه ای مبتنی بر ابر
عنوان انگلیسی مقاله: PAU: Privacy Assessment method with Uncertainty consideration for cloud-based vehicular networks
مجله/کنفرانس: سیستم های کامپیوتری نسل آینده-Future Generation Computer Systems
رشته های تحصیلی مرتبط: مهندسی کامپیوتر، مهندسی فناوری اطلاعات
گرایش های تحصیلی مرتبط: رایانش ابری، امنیت اطلاعات، شبکه های کامپیوتری
کلمات کلیدی فارسی: شبکه نقلیه ای مبتنی بر ابر، حریم خصوصی، عدم قطعیت، V2X
کلمات کلیدی انگلیسی: Cloud-based Vehicular network, privacy, uncertainty, V2X
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.future.2019.02.038
دانشگاه: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
صفحات مقاله انگلیسی: 24
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 7.007 در سال 2018
شاخص H_index: 93 در سال 2019
شاخص SJR: 0.835 در سال 2018
شناسه ISSN: 0167-739X
شاخص Quartile (چارک): Q1 در سال 2018
فرمت مقاله انگلیسی: PDF
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
کد محصول: E12080
فهرست مطالب (انگلیسی)

Abstract

1. Introduction

2. Related work

3. System model

4. System formalization

5. Performance evaluation in mix-zone

6. Conclusion

Acknowledgments

References

بخشی از مقاله (انگلیسی)

Abstract

With the rapid progress of wireless communication and big data, the traditional Vehicular Ad-hoc Networks (VANETs) gradually evolve into the new Heterogeneous Vehicular Networks (HetVNets). Meanwhile, with the combination of multiple forms of communication modes, it initiates the Vehicle to Everything(V2X) communication model providing more efficient services. V2X communication generates much more private data than traditional VANETs, but the concerns over privacy breaches are increasing. these big data burdens the concerns about. To protect the privacy in these cloud-based vehicular networks is remained unsolved. In this paper, we propose Privacy Assessment method with Uncertainty consideration (PAU) to estimate the nodes’ capability in protecting privacy, and then choose the vehicular nodes with high priority calculated by PAU to improve the whole network’s privacy protection level. PAU expands subjective logic based on two-tuple to triad and keeps uncertainty as a constituent element. It evaluates the nodes by using the historical data from the vehicular cloud and the real-time data from V2V communications. The experiments and analysis show that the improvement of privacy-preserving capability achieved when applied PAU in Mix-zone scenarios.

Introduction

Vehicular Ad-hoc Networks(VANETs) are envisaged to be one of the building blocks of the Internet of cognitive Things and accelerate the evolution of the Intelligent Transportation System(ITS). Based on Americans 5G white paper[1], vehicle-to-everything(V2X) communication model is mainly composed by Vehicleto-Vehicle(V2V), Vehicle-to-Infrastructure (V2I), Vehicle-to-Network(V2N) and Vehicle-to-Pedestrian(V2P). The heterogeneous mode [2] accelerates the efficiency of information dissemination. However, it adds the concerns about privacy breaches. The long-term storage of historical data on the cloud platform adds to the worries about privacy issues. The heterogeneous vehicular networks increase the difficulties of privacy protection . There are three main dimensions taken into account in traditional entropybased privacy assessment methods, the specific aspects or types of privacy, the adversary and capabilities, and the privacy metric[3][4][5]. Those assessment methods are all considered to be off-line, which are quantitatively evaluated based on specific information or privacy breaches. In the cloud-based V2X network environment, on the one hand, it is challenging to evaluate every event with the high-speed of information dissemination, on the other hand, the results of the offline evaluation couldn’t make up for the data leakage. In the information interaction, a node’s low awareness of privacy protection will lessen the privacy protection capability of the entire communication system. To track this problem, we propose the Privacy Assessment method with Uncertainty consideration (PAU) metric based on vehicular nodes uncertainty assessment.