Vehicular networks enable vehicles to generate and broadcast messages in order to improve traffic safety and efficiency. However, due to the nontrusted environments, it is difficult for vehicles to evaluate the credibilities of received messages. In this paper, we propose a decentralized trust management system in vehicular networks based on blockchain techniques. In this system, vehicles can validate the received messages from neighboring vehicles using Bayesian Inference Model. Based on the validation result, the vehicle will generate a rating for each message source vehicle. With the ratings uploaded from vehicles, roadside units (RSUs) calculate the trust value offsets of involved vehicles and pack these data into a “block.” Then, each RSU will try to add their “blocks” to the trust blockchain which is maintained by all the RSUs. By employing the joint proof-of-work (PoW) and proof-of-stake consensus mechanism, the more total value of offsets (stake) is in the block, the easier RSU can find the nonce for the hash function (PoW). In this way, all RSUs collaboratively maintain an updated, reliable, and consistent trust blockchain. Simulation results reveal that the proposed system is effective and feasible in collecting, calculating, and storing trust values in vehicular networks.

Blockchain-Based Decentralized Trust Management in Vehicular Networks

Safety is the most important requirement for autonomous vehicles; hence, the ultimate challenge of designing an edge computing ecosystem for autonomous vehicles is to deliver enough computing power, redundancy, and security so as to guarantee the safety of autonomous vehicles. Specifically, autonomous driving systems are extremely complex; they tightly integrate many technologies, including sensing, localization, perception, decision making, as well as the smooth interactions with cloud platforms for high-definition (HD) map generation and data storage. These complexities impose numerous challenges for the design of autonomous driving edge computing systems. First, edge computing systems for autonomous driving need to process an enormous amount of data in real time, and often the incoming data from different sensors are highly heterogeneous. Since autonomous driving edge computing systems are mobile, they often have very strict energy consumption restrictions. Thus, it is imperative to deliver sufficient computing power with reasonable energy consumption, to guarantee the safety of autonomous vehicles, even at high speed. Second, in addition to the edge system design, vehicle-to-everything (V2X) provides redundancy for autonomous driving workloads and alleviates stringent performance and energy constraints on the edge side. With V2X, more research is required to define how vehicles cooperate with each other and the infrastructure. Last, safety cannot be guaranteed when security is compromised. Thus, protecting autonomous driving edge computing systems against attacks at different layers of the sensing and computing stack is of paramount concern. In this paper, we review state-of-the-art approaches in these areas as well as explore potential solutions to address these challenges.

Edge Computing for Autonomous Driving: Opportunities and Challenges

Vehicular ad hoc networks (VANETs) are becoming the most promising research topic in intelligent transportation systems, because they provide information to deliver comfort and safety to both drivers and passengers. However, unique characteristics of VANETs make security, privacy, and trust management challenging issues in VANETs’ design. This survey article starts with the necessary background of VANETs, followed by a brief treatment of main security services, which have been well studied in other fields. We then focus on an in-depth review of anonymous authentication schemes implemented by five pseudonymity mechanisms. Because of the predictable dynamics of vehicles, anonymity is necessary but not sufficient to thwart tracking an attack that aims at the drivers’ location profiles. Thus, several location privacy protection mechanisms based on pseudonymity are elaborated to further protect the vehicles’ privacy and guarantee the quality of location-based services simultaneously. We also give a comprehensive analysis on various trust management models in VANETs. Finally, considering that current and near-future applications in VANETs are evaluated by simulation, we give a much-needed update on the latest mobility and network simulators as well as the integrated simulation platforms. In sum, this paper is carefully positioned to avoid overlap with existing surveys by filling the gaps and reporting the latest advances in VANETs while keeping it self-explained.

A Survey on Recent Advances in Vehicular Network Security, Trust, and Privacy

Throughout the last century, the automobile industry achieved remarkable milestones in manufacturing reliable, safe, and affordable vehicles. Because of significant recent advances in computation and communication technologies, autonomous cars are becoming a reality. Already autonomous car prototype models have covered millions of miles in test driving. Leading technical companies and car manufacturers have invested a staggering amount of resources in autonomous car technology, as they prepare for autonomous cars’ full commercialization in the coming years. However, to achieve this goal, several technical and nontechnical issues remain: software complexity, real-time data analytics, and testing and verification are among the greater technical challenges; and consumer stimulation, insurance management, and ethical/moral concerns rank high among the nontechnical issues. Tackling these challenges requires thoughtful solutions that satisfy consumers, industry, and governmental requirements, regulations, and policies. Thus, here we present a comprehensive review of state-of-the-art results for autonomous car technology. We discuss current issues that hinder autonomous cars’ development and deployment on a large scale. We also highlight autonomous car applications that will benefit consumers and many other sectors. Finally, to enable cost-effective, safe, and efficient autonomous cars, we discuss several challenges that must be addressed (and provide helpful suggestions for adoption) by designers, implementers, policymakers, regulatory organizations, and car manufacturers.

Autonomous Cars: Research Results, Issues, and Future Challenges

Privacy and Freedom.

Safety-critical applications in cooperative vehicular networks require authentication of nodes and messages. Yet, privacy of individual vehicles and drivers must be maintained. Pseudonymity can satisfy both security and privacy requirements. Thus, a large body of work emerged in recent years, proposing pseudonym solutions tailored to vehicular networks. In this survey, we detail the challenges and requirements for such pseudonym mechanisms, propose an abstract pseudonym lifecycle, and give an extensive overview and categorization of the state of the art in this research area. Specifically, this survey covers pseudonym schemes based on public key and identity-based cryptography, group signatures and symmetric authentication. We compare the different approaches, give an overview of the current state of standardization, and identify open research challenges.

Pseudonym Schemes in Vehicular Networks: A Survey

Because of the potential impact on user's life in cooperative automated safety applications, the security of Vehicle-to-X communication (V2X) is mandatory. However, the current attacker model used in literature is often too network-oriented, and it is unclear what realistic attacks could be. In this paper, we use the V2X data lifecycle to derive the attack surfaces. From this, we lay the foundations of a revisited attacker model, which details realistic attacks and identify appropriate countermeasures. We demonstrate that while the security of data processing, data at rest, and data in-transit is well-advanced, the security of meta-data and data acquisition requires extra attention by the research community.

Revisiting attacker model for smart vehicles

Current standardization efforts for cooperative Intelligent Transportation Systems both in the U.S. and Europe foresee vehicles to use a large number of changeable pseudonyms for privacy protection. Provisioning and storage of these pseudonyms require efficient and secure mechanisms to prevent malicious use of pseudonyms. In this paper we investigate several techniques to improve secure and efficient storage of pseudonyms. Specifically, we propose schemes based on Physical Unclonable Functions (PUFs) that allow to replace expensive secure key storage by regular unsecured memory and still provide fully secure pseudonyms storage.

Efficient and secure storage of private keys for pseudonymous vehicular communication

Most proposals for security of vehicular networks foresee the generation of a comparatively large number of changing pseudonyms to prevent vehicles from being identified or tracked. Most proposals rely on communication with backend pseudonym providers to refill a vehicle's pseudonym pool which creates a number of problems, one being secure storage and handling of a large amount of private key material. In this paper we investigate the usage of Physical Unclonable Functions (PUFs) and Public PUFs (PPUFs) instead of Hardware Security Modules for this purpose. We describe a possible solution that uses PUF and Fuzzy Extractors to provide the necessary stability.

/pdf/on-the-potential-of-puf-for-pseudonym-generation-in-12hmfeq7f8.pdf

On the potential of PUF for pseudonym generation in vehicular networks

In the context of vehicular networks, certificate management is challenging because of the dynamic topology and privacy requirements. In this paper we propose a technique that combines certificate omission and certificate pre-distribution in order to reduce communication overhead and to minimize cryptographic packet loss. Simulation results show that this technique is useful to improve awareness quality during pseudonym changes.

Pre-Distribution of Certificates for Pseudonymous Broadcast Authentication in VANET

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