Privacy Of Vehicles Research Articles

Vehicloak: A Blockchain-Enabled Privacy-Preserving Payment Scheme for Location-Based Vehicular Services

The Internet of Vehicles (IoV) technology enables vehicles to communicate with each other, with pedestrians and with roadside infrastructures, to realize more efficient, safer and more environmentally friendly transportation. IoV also promises rich location-based services for vehicles, such as parking and toll highway. However, preserving privacy for location-based service payments emerges as a critical and challenging problem in IoV. Existing schemes rely on centralized banks for payment processing, resulting in location privacy leakage to centralized entities. In this paper, we propose a decentralized privacy-preserving payment scheme named Vehicloak for IoV based on the blockchain technology. The biggest challenge is to provide location privacy for vehicles while guaranteeing correct service payments using the transparent blockchain. To tackle this challenge, we introduce a new cryptographic technique called zk-GSigproof that integrates zero-knowledge proof with group signature. Vehicloak implements this technique in a smart contract to process payment, which verifies zero-knowledge proof and group signature without leaking location information. It is not limited to IoV and can be applied in many payment scenarios. To evaluate the performance of our scheme, we implement Vehicloak on a private blockchain of 100 nodes on Aliyun, and conduct a test with up to 4,000 transactions. The experimental results prove the feasibility of Vehicloak.

A Conditional Privacy-Preserving Certificateless Aggregate Signature Scheme in the Standard Model for VANETs

Vehicular ad hoc networks (VANETs) are the communication foundation for future intelligent transportation systems and guarantee safe driving of intelligent networked vehicles. Moreover, VANETs face a series of security challenges related to the protection of vehicle privacy, authenticity of transmitted information, and bandwidth limitations. To resolve these contradictions, many certificateless aggregate signature (CLAS) schemes have been proposed. However, the majority of them can neither resist malicious-but-passive key generation center attacks, replay attacks, and link attacks, nor track the actual identities of malicious vehicles. Meanwhile, the security of previous CLAS schemes in VANETs is only formally provided in the random oracle model (ROM), which might be insecure in actual implementation. In addition, most CLAS schemes still have problems of large verification delays and high communication overhead. To address the above-mentioned problems, a new conditional privacy-preserving CLAS scheme in VANETs is proposed, which adopts full aggregation technology to reduce computation and bandwidth resources. According to the formal security proofs under the computational Diffie-Hellman problem (CDHP) given in the standard model (SM), the security level of this scheme is higher than that of other CLAS schemes under ROM in practical applications. Additionally, the use of pseudonym mechanism realizes conditional privacy protection in VANETs. The performance analysis shows that this scheme has a higher efficiency in terms of computation and communication overhead compared with several previous CLAS schemes.

Security Analysis for a VANET Privacy Protection Scheme

Vehicular ad hoc network (VANET) is a self-organizing wireless sensor network model, which is extensively used in the existing traffic. Due to the openness of wireless channel and the sensitivity of traffic information, data transmission process in VANET is vulnerable to leakage and attack. Authentication of vehicle identity while protecting vehicle privacy information is an advantageous way to improve the security of VANET. We propose a scheme based on fair blind signature and secret sharing algorithm. In this paper, we prove that the scheme is feasible through security analysis.

Optimal computational resource pricing in vehicular edge computing: A Stackelberg game approach

Vehicular edge computing (VEC) pushes the computational resources to the logical edge of the networks, thus enabling vehicles to run resource-hungry and time-sensitive applications by outsourcing operations. Many studies revolved around VEC focus on the optimization of response latency, energy consumption, or both of them, assuming that the computational resources in VEC can be utilized for free. However, VEC provisions computational resources on a pay-as-you-go basis, which means VEC can obtain revenues by leasing the computational resources. In this paper, we focus on the real-time computational resource pricing in VEC, in the hope to reach a win–win situation where both VEC and vehicles can optimize their respective utility values. To reach such a mutually satisfactory result, we adopt the Stackelberg game to model the computational resource pricing problem in this paper. In this game, vehicles are followers and the edge server serves as the leader. Furthermore, we have proven that a unique Stackelberg equilibrium exists in the proposed pricing game. A distributed algorithm is put forward to solve our problem, which considers the privacy of vehicles. The distributed approach is evaluated by extensive experiments, in terms of convergence rate, running time and so on. The simulation results demonstrate that the distributed approach can achieve satisfactory results without privacy disclosure compared to the centralized approach.

Blockchain-Based Secured IPFS-Enable Event Storage Technique With Authentication Protocol in VANET

In recent decades, intelligent transportation systems (ITS) have improved drivers' safety and have shared information (such as traffic congestion and accidents) in a very efficient way. However, the privacy of vehicles and the security of event information is a major concern. The problem of secure sharing of event information without compromising the trusted third party (TTP) and data storage is the main issue in ITS. Blockchain technologies can resolve this problem. A work has been published on blockchain-based protocol for secure sharing of events and authentication of vehicles. This protocol addresses the issue of the safe storing of event information. However, authentication of vehicles solely depends on the cloud server. As a result, their scheme utilizes the notion of partially decentralized architecture. This paper proposes a novel decentralized architecture for the vehicular ad-hoc network (VANET) without the cloud server. This work also presents a protocol for securing event information and vehicle authentication using the blockchain mechanism. In this protocol, the registered user accesses the event information securely from the interplanetary file system (IPFS). We incorporate the IPFS, along with blockchain, to store the information in a fully distributed manner. The proposed protocol is compared with the state-of-the-art. The comparison provides desirable security at a reasonable cost. The evaluation of the proposed smart contract in terms of cost (GAS) is also discussed.

A Distributed Mix-Context-Based Method for Location Privacy in Road Networks

Preserving location privacy is increasingly an essential concern in Vehicular Adhoc Networks (VANETs). Vehicles broadcast beacon messages in an open form that contains information including vehicle identity, speed, location, and other headings. An adversary may track the various locations visited by a vehicle using sensitive information transmitted in beacons such as vehicle identity and location. By matching the vehicle identity used in beacon messages at various locations, an adversary learns the location history of a vehicle. This compromises the privacy of the vehicle driver. In existing research work, pseudonyms are used in place of the actual vehicle identity in the beacons. Pseudonyms should be changed regularly to safeguard the location privacy of vehicles. However, applying simple change in pseudonyms does not always provide location privacy. Existing schemes based on mix zones operate efficiently in higher traffic environments but fail to provide privacy in lower vehicle traffic densities. In this paper, we take the problem of location privacy in diverse vehicle traffic densities. We propose a new Crowd-based Mix Context (CMC) privacy scheme that provides location privacy as well as identity protection in various vehicle traffic densities. The pseudonym changing process utilizes context information of road such as speed, direction and the number of neighbors in transmission range for the anonymisation of vehicles, adaptively updating pseudonyms based on the number of a vehicle neighbors in the vicinity. We conduct formal modeling and specification of the proposed scheme using High-Level Petri Nets (HPLN). Simulation results validate the effectiveness of CMC in terms of location anonymisation, the probability of vehicle traceability, computation time (cost) and effect on vehicular applications.

Privacy-preserving location-based traffic density monitoring

Traffic density monitoring is an important method to predict road traffic conditions, which can bring some convenience to people's travel in daily life. The common method of traffic density monitoring is to collect and process the location information uploaded by vehicles, but the information of these vehicle location contains a large amount of personal privacy information of vehicle owners, and there is a risk of privacy disclosure. In this paper, we propose a traffic density monitoring system by adding a pseudonym server and a location anonymisation server; the identity information and location information of the vehicles are saved separately. The system can protect both the location privacy of vehicles and the query privacy of users. To prevent dummy locations from being filtered, we calculate the probability distribution of historical location service requests to generate location anonymous sets, which can improve the success rate of anonymity. The location anonymisation server uses the location anonymous set instead of the real location of the vehicle to send to the location-based service provider, which can increase the location privacy security of the vehicle. According to the experimental results of this paper, compared with SimpMaxMinDistds algorithm and MMDS algorithm, our system has better location anonymous set generation efficiency and location privacy protection level.

LPC: A lightweight pseudonym changing scheme with robust forward and backward secrecy for V2X

Vehicle-to-everything (V2X) has been regarded as an important enabling technology for future intelligent transportation systems (ITS). Safety-critical applications in V2X require authentication of nodes and messages, and also need to preserve the privacy of vehicles. Pseudonymity plays an important role in V2X communication by satisfying both security and privacy requirements. In this paper, we analyze the weaknesses of a recent relevant work named Issue First Activate Later (IFAL), which is the leading European candidate for V2X. We then propose a novel lightweight, secure and privacy-preserving pseudonym changing scheme. Through the security analysis, we show that the proposed scheme achieves the asynchronous key agreement, and realizes both robust forward secrecy and backward secrecy while keeping security functionalities of IFAL. Furthermore, the performance analysis is conducted on Arduino Mega 2560 to illustrate that the proposed scheme is feasible and practical. © 2014 xxxxxxxx. Hosting by Elsevier B.V. All rights reserved.

Federated learning-based scheme for detecting passive mobile attackers in 5G vehicular edge computing

Detecting passive attacks is always considered difficult in vehicular networks. Passive attackers can eavesdrop on the wireless medium to collect beacons. These beacons can be exploited to track the positions of vehicles not only to violate their location privacy but also for criminal purposes. In this paper, we propose a novel federated learning-based scheme for detecting passive mobile attackers in 5G vehicular edge computing. We first identify a set of strategies that can be used by attackers to efficiently track vehicles without being visually detected. We then build an efficient machine learning (ML) model to detect tracking attacks based only on the receiving beacons. Our scheme enables federated learning (FL) at the edge to ensure collaborative learning while preserving the privacy of vehicles. Moreover, FL clients use a semi-supervised learning approach to ensure accurate self-labeling. Our experiments demonstrate the effectiveness of our proposed scheme to detect passive mobile attackers quickly and with high accuracy. Indeed, only 20 received beacons are required to achieve 95% accuracy. This accuracy can be achieved within 60 FL rounds using 5 FL clients in each FL round. The obtained results are also validated through simulations.

Blockchain-Based Pseudonym Management Scheme for Vehicular Communication

A vehicular ad hoc network (VANET) consists of vehicles, roadside units, and other infrastructures that communicate with each other with the goal of improving road safety, reducing accidents, and alleviating traffic congestion. For safe and secure operation of critical applications in VANET, it is essential to ensure that only authenticated vehicles can participate in the network. Another important requirement for VANET communication is that the privacy of vehicles and their users must be protected. Privacy can be improved by using pseudonyms instead of actual vehicle identities during communication. However, it is also necessary to ensure that these pseudonyms can be linked to the real vehicle identities if needed, in order to maintain accountability. In this paper, we propose a new blockchain-based decentralized pseudonym management scheme for VANET. This allows the vehicles to maintain conditional anonymity in the network. The blockchain is used to maintain a record of each vehicle and all of its pseudo-IDs. The information in the blockchain can only be accessed by authorized entities and is not available to all vehicles. The proposed distributed framework maintains an immutable record of the vehicle data, which is not vulnerable to a single point of failure. We compared the performance of the proposed approach with a traditional PKI scheme and shown that it significantly reduces the authentication delay.

Privacy-Preserving Smart Road-Pricing System with Trustworthiness Evaluation in VANETs.

With the advanced development of the intelligent transportation system, vehicular ad hoc networks have been observed as an excellent technology for the development of intelligent traffic management in smart cities. Recently, researchers and industries have paid great attention to the smart road-tolling system. However, it is still a challenging task to ensure geographical location privacy of vehicles and prevent improper behavior of drivers at the same time. In this paper, a reliable road-tolling system with trustworthiness evaluation is proposed, which guarantees that vehicle location privacy is secure and prevents malicious vehicles from tolling violations at the same time. Vehicle route privacy information is encrypted and uploaded to nearby roadside units, which then forward it to the traffic control center for tolling. The traffic control center can compare data collected by roadside units and video surveillance cameras to analyze whether malicious vehicles have behaved incorrectly. Moreover, a trustworthiness evaluation is applied to comprehensively evaluate the multiple attributes of the vehicle to prevent improper behavior. Finally, security analysis and experimental simulation results show that the proposed scheme has better robustness compared with existing approaches.

A Location Privacy Preservation Method Based on Dummy Locations in Internet of Vehicles

During the procedure, a location-based service (LBS) query, the real location provided by the vehicle user may results in the disclosure of vehicle location privacy. Moreover, the point of interest retrieval service requires high accuracy of location information. However, some privacy preservation methods based on anonymity or obfuscation will affect the service quality. Hence, we study the location privacy-preserving method based on dummy locations in this paper. We propose a vehicle location privacy-preservation method based on dummy locations under road restriction in Internet of vehicles (IoV). In order to improve the validity of selected dummy locations under road restriction, entropy is used to represent the degree of anonymity, and the effective distance is introduced to represent the characteristics of location distribution. We present a dummy location selection algorithm to maximize the anonymous entropy and the effective distance of candidate location set consisting of vehicle user’s location and dummy locations, which ensures the uncertainty and dispersion of selected dummy locations. The proposed location privacy-preservation method does not need a trustable third-party server, and it protects the location privacy of vehicles as well as guaranteeing the LBS quality. The performance analysis and simulation results show that the proposed location privacy-preservation method can improve the validity of dummy locations and enhance the preservation of location privacy compared with other methods based on dummy locations.

Cooperation and radio silence strategy in Mix Zone to Protect Location Privacy of Vehicle in VANET

With increase in the population, the number of registered vehicles has dramatically increased over all the world, and this leads to a high rate of traffic accidents on the roads. Therefore, in order to prevent such accidents, an Intelligent Transportation Systems (ITSs) is needed to be installed to notify drivers of obstacles in advance. Recently, the Internet of things (IoT) evolves the vehicular communications and covers this technology under the Internet of vehicles (IoV) application. IoV is a new field for the automotive industry and a significant part of the smart cities. However, protecting the privacy of vehicle's location is the most challenging subject in the vehicular communication, as because it threatens the personal life of drivers. This paper provides cooperation and radio silence strategy in mix zone (CRSMZ) to protect location privacy of vehicle in IoV. The strategy implements either cooperation or radio silence depending on the speed of the vehicle while it is in mix _zone. The simulation results show that CRSMZ is an efficient strategy to protect location information of vehicle drivers. CRSMZ outperforms the existing strategies in terms of mean of the number tracker confusion, continuous tracking period and max of the entropy.

Guest Editorial Introduction to the Special Section on Blockchain for Vehicles and Intelligent Communications

The papers in this special section focus on blockchain for vehicles and intelligent communications. The proliferation of vehicles and intelligent communications is generating enormous amount of data for enabling autonomous driving and innovative communications applications. The data may raise efficiency and security concerns to users, e.g., offering real-time services for smart phones/devices/vehicles and exposing sensitive driving information to the untrusted 3rd party. Blockchain, which is featured with decentralization recording and tamper-proofing, has been emerged as a promising approach for mitigating data security risks of vehicles and intelligent communications. In addition, incorporating with smart contracts and other techniques, users connected through blockchain can share the data information in an efficient, private and secure manner, thereby reducing the management cost of service provision, improving the security of intelligent communications, and protecting the privacy of vehicles and mobile users. Further, highly dynamic communication topology caused by driving vehicles may undermine the establishment of consensus protocols. Thus, efficiency will become a critical challenge in leveraging blockchain for delay sensitive services in vehicular and intelligent communications.

A Novel Pseudonym Assignment and Encryption Scheme for Preserving the Privacy of Military Vehicles

In this digital era, security has become one of the important topics of concern, and things become more critical for military vehicles where safety plays a vital role. In this paper, we have discussed a pseudonym-based approach that preserves the real identity of military vehicles. This paper also focuses on military vehicles’ location privacy by deploying a novel pseudonym assignment and encryption schemes. The proposed security scheme is based on a hybrid approach of matrix array symmetric key and the intelligent water drop scheme. After implementing the proposed security scheme, each military vehicle will obtain its pseudonym for hiding their original identities. The proposed algorithm effectively manages pseudonym generation and change requests for the local region and inter-region environment. The proposed security scheme not only provides secure communication and preservation of location privacy of military vehicles but also ensures their security against various attacks. Finally, the time efficiency of proposed algorithms is obtained for both local and inter-region requests. Comparative analysis shows that the proposed scheme is more efficient than other existing techniques.

Secure crowd-sensing protocol for fog-based vehicular cloud

The new paradigm of fog computing was extended from conventional cloud computing to provide computing and storage capabilities at the edge of the network. Applied to vehicular networks, fog-enabled vehicular computing is expected to become a core feature that can accelerate a multitude of services including crowd-sensing. Accordingly, the security and privacy of vehicles joining the crowd-sensing system have become important issues for cyber defense and smart policing. In addition, to satisfy the demand of crowd-sensing data users, fine-grained access control is required. In this paper, we propose a secure and privacy-preserving crowd-sensing scheme for fog-enabled vehicular computing. The proposed architecture is made by a double layer of fog nodes that is used to generate crowd-sensing tasks for vehicles, then collect, aggregate and analyze the data based on user specifications. To ensure data confidentiality and fined-grained access control, we make use of ciphertext-policy attribute-based encryption with access update policy (CP-ABE-UP), which is a well-known one-to-many encryption technique. The policy update algorithm allows the fog nodes to outsource the crowd-sensing data to other fog nodes or to data users directly. We also adopted the ID-based signature tied to pseudonymous techniques to guarantee the authentication and privacy-preservation of the entities in the system. From the upper fog layer to the data user, we show that an information-centric networking (ICN) approach can be applied to maximize the network resources and enhance the security by avoiding unauthorized and unauthenticated data owners. The security analysis confirms that our approach is secure against known attacks, whereas the simulation results show its efficiency in terms of communication with little computational overhead.

A Privacy-Preserving Key Management Scheme with Support for Sybil Attack Detection in VANETs.

Vehicular ad hoc networks (VANETs) face two important and conflicting challenges with regards to security: preserve the privacy of vehicles in order to prevent malicious entities from tracking users and detect and remove bad actors that attempt to game the system for their own advantage. In particular, detecting Sybil attacks, in which one node attempts to appear as many, seemingly conflicts with the goal of privacy preservation, and existing schemes fail on either one or both accounts. To fill this gap, we present a hierarchical key management system which uses short group signatures to preserve member privacy at lower levels while allowing mid-level nodes to detect Sybil attacks and highly trusted nodes at the top of the hierarchy to completely reveal the real identities of malicious nodes in order to prevent them from rejoining the system and for use by legal authorities. In addition, we present an argument for relaxing the requirement of backward secrecy in VANET groups in the case when no malicious activity has been detected.

A secure and distributed architecture for vehicular cloud

Given the enormous interest in Internet of Things (IoT), Vehicular Ad hoc NETworks (VANETs) are likely to be widely deployed in the near future. Cloud computing is also gaining widespread deployment. Marriage between cloud computing and VANETs, would help solve many of the needs of the drivers, law-enforcement agencies, traffic management, etc. In this paper, we propose a secure and distributed architecture for vehicular cloud which uses the capabilities of vehicles to provide various services such as parking management, accident alert, traffic updates, cooperative driving, etc. Our architecture ensures privacy of vehicles, and supports scalable and secure message dissemination using vehicular infrastructure.

A location privacy protection scheme for convoy driving in autonomous driving era

Convoy driving has great potential in the development of autonomous driving industry, which can bring great improvement to the utilization rate of roads and the travel experience of passengers. However, the emergence of convoy driving also brings some new challenges to the location privacy of autonomous vehicles. In this paper, a novel dynamic mixed zone establishment scheme is proposed to protect the location privacy of autonomous vehicles in convoy driving context. As the convoy is a closed group, the request for the establishment of the mix zone will be broadcast first within the convoy, followed by outside the convoy. In order to prevent pseudonym syntactic join attacks within the convoy, the proposed scheme allows autonomous vehicles to use multiple valid pseudonyms if only itself change the pseudonym in the convoy. In order to trace the real identity of the offending vehicle, the proposed scheme specifies the distribution method of the pseudonym, the legitimate vehicle can trace the real identity of the offending vehicle by submitting the pseudonym of the offending vehicle. Compared with the scheme to protect location privacy in the traditional vehicular network, the proposed scheme has less overhead and a higher level of security.

CPAAS: an efficient conditional privacy-preservation anonymous authentication scheme using signcryption in VANET

Vehicular Ad-Hoc Network (VANET) is a tremendously promising innovation in the field of Intelligent Transportation System (ITS). It facilitates a vehicle to communicate on the road with infrastructure or OBU to increase road safety and driving conditions. Further, to deliver the same requires a secure network build-up through a legitimate entity. However, in an open network, any of the participating entities may be malicious. Therefore we need a proper solution to mitigate the effects of such entities by authenticating them, and it should also preserve the privacy of entity and integrity of message so that the impact of any malicious entity can be nullified. The existing solutions focus on any of the above aspects, but not all, further they are not computationally efficient. Inspired by this, we propose a computationally efficient approach that achieves most of the security requirements with less communication overhead. We have used distributed pseudo_id for preserving the privacy of vehicle and the ID-based Certificateless Signcryption scheme based on elliptic curve cryptography to provide an effective way to achieve mutual authentication, integrity, non-repudiation, and confidentiality. The security analysis of our approach demonstrates the efficiency and effectiveness of our proposed scheme.