Achieving Privacy-Preserving Vehicle Selection for Effective Content Dissemination in Smart Cities

Yunguo Guan, Rongxing Lu, Yandong Zheng, Jun Shao, Guiyi Wei
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Abstract

By integrating various connected devices, it is possible for smart cities to optimize the efficiency of various aspects of city operations. In particular, connected vehicles in smart cities, which are coordinated by Intelligent Transportation Systems (ITS), can not only enjoy enhanced safety and efficiency, but also offer content dissemination services through smart cities. In order to achieve effective content dissemination, a vehicle selection approach usually needs to be involved to select a limited number of vehicles while disseminating content to a city as wide as possible. However, such an approach inevitably requires the trajectories of vehicles, which are private to the vehicles. In this paper, to preserve the trajectory privacy of the vehicles during the vehicle selection, we propose a privacy-preserving vehicle selection scheme for effective content dissemination. Specifically, in the proposed scheme, given encrypted trajectories of $n$ vehicles, a cloud with two non-collusive servers can select $k$ vehicles that jointly cover an approximately optimal area of the city. Detailed security analysis and performance evaluation show that our proposed scheme can not only preserve the privacy of vehicles' trajectories, but also achieve efficient vehicle selection with an approximately optimal coverage.
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通过整合各种互联设备,智慧城市可以优化城市运营各个方面的效率。特别是智慧城市中的网联车辆,通过智能交通系统(ITS)进行协调,不仅可以提高安全性和效率,还可以通过智慧城市提供内容传播服务。为了实现有效的内容传播,通常需要涉及车辆选择方法,选择有限数量的车辆,同时尽可能广泛地将内容传播到一个城市。然而,这种方法不可避免地需要车辆的轨迹,这对车辆来说是私有的。为了在车辆选择过程中保护车辆的轨迹隐私,本文提出了一种保护隐私的车辆选择方案,以实现有效的内容传播。具体来说,在提出的方案中,给定n辆车的加密轨迹,一个具有两个非串通服务器的云可以选择k辆车,这些车共同覆盖城市的大约最佳区域。详细的安全性分析和性能评估表明,该方案既能保护车辆轨迹的隐私性,又能以近似最优的覆盖范围实现高效的车辆选择。
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