Server-Assisted Data Sharing System Supporting Conjunctive Keyword Search for Vehicular Social Networks

Vehicular social networks (VSNs), as the convergence of social networks and vehicular ad hoc networks, have brought many useful services to vehicle communication by collecting and sharing data between vehicles. In order to efficiently share data and satisfy the growing requirement of privacy protection, data owners typically encrypt and outsource the data to the cloud. Nevertheless, encryption undoubtedly reduces the availability of shared data, e.g., keyword search. Although a number of schemes supporting keyword search of shared data have been put forward, they still have issues with respect to security, functionality, and efficiency. In this paper, a server-assisted data sharing (SADS) system with support for conjunctive keyword search is presented. Specifically, to resist online keyword guessing attack, we devise an advanced keyword derivation mechanism to derive the keyword set, in which the conception of verifiable parallel oblivious unpredictable function is proposed to check whether the assisted server honestly responds to the derived keyword request. Moreover, the computation and communication costs of keyword trapdoor in SADS are constant. Concurrently, SADS achieves the anonymous data sharing and traceability of malicious vehicle data owner. The security of SADS is formally proved and analyzed. Performance evaluation also shows that our system is efficient and practical.

I. Introduction

Vehicular ad hoc network (VANET), as a self-organized ad hoc network, offers convenient communication and network services to vehicles traveling on the road [1], [2]. With the rapid development of VANET, vehicles can now be regarded as mobile social spaces. Vehicular social networks (VSNs) [3], [4], as an emerging concept, are considered to be a combination of VANETs and social networks, emphasizing the social attributes of VANETs. By sharing sensory data between vehicles or vehicles and roadside infrastructures, VSNs provide user-friendly social services for vehicle users, in addition to intelligent traffic management, accurate road navigation, and rational traffic resource planning. However, massive amounts of VSNs data also bring challenges in terms of storage for vehicles with limited resources. As a result, the cloud server (CS) with sufficient storage and computing resources is introduced to store the ever-increasing amount of VSNs data, freeing up the local resources of the vehicles significantly. Therefore, the data is typically outsourced to the CS by the vehicle data owner (VDO) and can be downloaded later by the vehicle data user (VDU). Nevertheless, the vehicle's privacy may be revealed through sensitive information in outsourced data. Consequently, the privacy protection of outsourced data is necessary to be considered [3], [5].