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

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.