QBPP: Quality of services–based location privacy protection method for location-based services in cloud-enabled Internet of vehicles

Abstract

Location-based services has been widely applied in cloud-enabled Internet of vehicles. Within these services, location privacy issues have captured significant attention. Vehicles use the technology of anonymity to implement occultation, the location is not revealed. In this process, large-scale data transmissions can reduce the quality of services. In order to ensure location privacy and high-quality services, the cloud manager customizes virtual machines for vehicles to support location-based services according to the vehicles’ demands. To achieve better performance, this article presents a conditional anonymity method that does not use bilinear pairings to address the problem of privacy disclosure by using discrete logarithm problem and Diffie–Hellman problem. Moreover, asymmetric key algorithms are used in the Internet of vehicles environment to reduce the cost. To guarantee secure data transmission in Internet of vehicles, the batch validation technique is used to address data integrity. Our theoretical security analysis and experiments show that the proposed scheme is secure in compared attack models, such as impersonation attacks, replay attacks, the man-in-the-middle attacks, and so on. Our proposed scheme ensures the security requirements such as message authentication, location privacy protection, and traceability, while lowering transmission and computation cost.