Vehicular Privacy Research Articles

Secure and Lightweight Vehicular Privacy Preservation Scheme Under Fog Computing-Based IoVs

Secure and Lightweight Vehicular Privacy Preservation Scheme Under Fog Computing-Based IoVs

Privacy-Preserved Federated Learning for Autonomous Driving

In recent years, the privacy issue in Vehicular Edge Computing (VEC) has gained a lot of concern. The privacy problem is even more severe in autonomous driving business than the other businesses in VEC such as ordinary navigation. Federated learning (FL), which is a privacy-preserved strategy proposed by Google, has become a hot trend to solve the privacy problem in many fields including VEC. Therefore, we introduce FL into autonomous driving to preserve vehicular privacy by keeping original data in a local vehicle and sharing the training model parameter only with the help of MEC server. Moreover, different from the common assumption of honest MEC server and honest vehicle in former studies, we take the malicious MEC servers and malicious vehicles into account. First, we consider honest-but-curious MEC server and malicious vehicles and propose a traceable identity-based privacy preserving scheme to protect the vehicular message privacy where improved Dijk-Gentry-Halevi-Vaikutanathan (DGHV) algorithm is proposed and a blockchain-based Reputation-based Incentive Autonomous Driving Mechanism (RIADM) is adopted. Further, when the case comes to the non-credibility of both parties where semi-honest MEC server and malicious vehicles are considered, we propose an anonymous identity-based privacy preserving scheme to protect the identity privacy of vehicles with Zero-Knowledge Proof (ZKP). Based on the simulation of virtual autonomous driving based on real-world road images, it is verified that our proposes scheme can reduce 73.7 % training loss of autonomous driving, increase the accuracy to around 5.55 % while keeps effective privacy of message and identity under the threat of dishonest MEC server and vehicles.

CyberChain: Cybertwin Empowered Blockchain for Lightweight and Privacy-Preserving Authentication in Internet of Vehicles

Internet of Vehicles (IoVs) presents promising opportunities for vehicle to everything (V2X) applications, wherein authentication acts as the cornerstone to realize trustworthy vehicular context and to support advanced applications. However, existing authentication schemes mainly depend on centralized servers with both security and privacy issues. In this paper, we propose a CyberTwin (CT) empowered blockchain framework for authentication, namely CyberChain, to reduce both the communication and storage cost while maintaining vehicular privacy. By designing a blockchain system in the cyberspace, we decouple the consensus process from the physical world, so that the operation cost of blockchain can be reduced. A Privacy-Preserving Parallel Pedersen Commitment (P4C) algorithm is designed to protect the privacy of vehicles and accelerate the authentication process. To further enhance the operation efficiency of CyberChain, we propose a Diffused Practical Byzantine Fault Tolerance (DPBFT) mechanism to reach consensus in the cyberspace that can reduce consensus latency. The proposed cyberchain framework and the associated mechanisms are evaluated by qualitative analysis and simulations. The evaluation results demonstrated that the proposed cyberchain based framework significantly improves the authentication performance in terms of authentication latency, privacy, communication overhead and storage cost.

An Unlinkable Authenticated Key Agreement With Collusion Resistant for VANETs

Anonymous authentication is an important step to protect vehicular privacy and security in VANETs. To improve the efficiency of anonymous authentication in multiple security domains, VANETs integrated with blockchain attracts extensive attention. However, the existing blockchain-based authentication schemes cannot effectively achieve anonymity because colluded RSUs or vehicles can achieve linkability via the same retrieved record to destroy anonymity. To address the issue, we propose an unlinkable authenticated key agreement with collusion resistant for VANETs. For the unlinkability of V2R, firstly, a trusted authority generates multiple tickets that hide the vehicle's identity and shares them by the blockchain network. Then, vehicle generates different pseudonyms by homomorphic encryption, and RSU uses different tickets to authenticate them. Meanwhile, in view of the unlinkability of the V2V, anonymous authenticated key agreement based on homomorphic properties is designed to enable vehicles to achieve authentication without retrieving any vehicular information. Security analysis and performance evaluation show that our scheme has strict unlinkability and enhanced anonymity as well as improves the efficiency of V2R and V2V by 10% and 50%, respectively.

Perturbation-Hidden: Enhancement of Vehicular Privacy for Location-Based Services in Internet of Vehicles

The great development of smart networks enables Internet of Vehicles (IoV) as a promising paradigm to provide pervasive services, where privacy issues for location-based services (LBSs) have attracted considerable attention. In terms of location privacy, inspired by differential privacy, geo-indistinguishability (Geo-Ind) has recently become a prevalent privacy model for LBSs. Although Geo-Ind guarantees the location privacy, users’ other privacy concerns are still at risk if the location perturbation behavior is exposed due to implausible reported locations. Through experiments we find the probability that the classical Geo-Ind mechanism perturbs the true location to implausible areas can be more than 50%. To address it, we first propose an enhanced privacy definition beyond Geo-Ind, called Perturbation-Hidden, to prevent location perturbation behaviors of users from being recognized by guaranteeing their pseudo-locations plausible. Then we design a mechanism to achieve this definition by transplanting the differential private exponential mechanism to our approach. Furthermore, we propose a method for determining the retrieval area utilizing dynamic programming to ensure the accuracy of LBSs. Finally, we theoretically prove that our mechanism satisfies the privacy definition. Extensive experiments on simulations and a real-world dataset show that our proposal achieves 100% plausible pseudo-locations while ensuring high query precision and recall.

A novel conditional anonymity scheme for vehicular communication networks

SummaryVehicular communication networks are traffic applications of the Internet of Things consisting of vehicle nodes, roadside units, service providers, and other components. To protect the vehicle privacy, some vehicular communication networks adopt anonymous schemes. However, in these anonymous schemes, anonymity abuse is inevitable. In order to protect vehicular privacy and prevent anonymity abuse, some conditional anonymous authentication schemes based on cryptography have been proposed. However, most of these methods have complex computation and large communication overhead, which makes them difficult to meet the actual requirement of high‐speed traffic in vehicular communication networks. In this paper, a novel conditional anonymity scheme called VKPCA (vehicular communication network based on kernel principal component analysis [KPCA]) is proposed, which can protect vehicle privacy while avoid anonymity abuse. The simulation results show that this scheme has lower computational complexity and communication overhead than the conventional anonymous authentication scheme.