Secure and privacy-aware traffic information as a service in VANET-based clouds

Abstract

The multitude of applications and services in Vehicular Ad hoc NETwork (VANET) and cloud computing technology that are dreamed of in the near future, are visible today. VANET applications are set to explode in the next couple of years as a result of the advancements in the wireless communication technologies and automobile industry. Nevertheless, it is speculated that future high-end vehicles will potentially under-utilize their on-board storage, computation, and communication resources. This phenomenon sets the ground for the evolution of traditional VANET to a rather more applications-rich paradigm referred to as VANET-based clouds. In this paper, we aim at a framework of VANET-based clouds namely VANET using Clouds (VuC) and propose a novel secure and privacy-aware service referred to as Traffic Information as a Service (TIaaS) atop the cloud computing services stack. TIaaS provides vehicles (more precisely subscribers) with fine-grained traffic information from the cloud as a result of subscribers' cooperation with the cloud in a privacy-preserving way. Legitimate VANET users share their frequent whereabouts information referred to as Mobility Vectors (MV) with the cloud infrastructure through gateways (static Road Side Units-RSUs and mobile vehicles with 3/4 G Internet). The gateways forward coarse-grained traffic information (MVs) from vehicles to the cloud whereas after processing, cloud modules construct and re-forward the fine-grained traffic information along with location-based warnings to the subscribers based on their physical locations and moving directions. The communication among vehicles, gateways, and the cloud infrastructure is carried out in a privacy-preserving way. More precisely vehicles share their MVs with cloud infrastructure anonymously. The MVs are hard to link to the sender, until and unless necessary, otherwise. Similarly every vehicle receives fine-grained traffic information in a privacy-preserving manner. The proposed TIaaS keeps the adversaries at bay from abusing users' privacy and/or constructing profiles against targeted users. Moreover for location confidentiality and privacy, we also propose a novel location-based encryption technique that keeps the insider and outsider adversaries at bay from manipulating the contents of the message. Furthermore, the proposed TIaaS preserves conditional privacy and with the help of an efficient revocation mechanism, revocation authorities can revoke any node in case of a dispute. The proposed TIaaS also introduces the thin-client concept for vehicles where most of the time-consuming processing is offloaded to the cloud and the processing resources of the vehicles can be used elsewhere, for instance for the critical safety related applications. More precisely the cloud processes the big traffic data (BTD) and produces timely, decisive, and meaningful results. (C) 2015 Elsevier B.V. All rights reserved.