相关论文: Privacy-Preserving Vehicular Announcements Aggrega…

The gradual fusion of intelligent transportation systems with metaverse technologies is giving rise to vehicular metaverses, which blend virtual spaces with physical space. As indispensable components for vehicular metaverses, Vehicular…

We estimate vehicular traffic states from multimodal data collected by single-loop detectors while preserving the privacy of the individual vehicles contributing to the data. To this end, we propose a novel hybrid differential privacy (DP)…

Personal monitoring devices such as cyclist helmet cameras to record accidents or dash cams to catch collisions have proliferated, with more companies producing smaller and compact recording gadgets. As these devices are becoming a part of…

Pseudonymisation provides the means to reduce the privacy impact of monitoring, auditing, intrusion detection, and data collection in general on individual subjects. Its application on data records, especially in an environment with…

Vehicular Ad hoc Networks (VANETs) are becoming a reality where secure communication is a prerequisite. Public key infrastructure (PKI) can be used to secure VANETs where an onboard tamper proof device (TPD) stores a number of encryption…

Vehicular networks are networks of communicating vehicles, a major enabling technology for future cooperative and autonomous driving technologies. The most important messages in these networks are broadcast-authenticated periodic one-hop…

The flying ad hoc network (FANET) will play a crucial role in the B5G/6G era since it provides wide coverage and on-demand deployment services in a distributed manner. The detection of Sybil attacks is essential to ensure trusted…

The advancements in autonomous driving technology, coupled with the growing interest from automotive manufacturers and tech companies, suggest a rising adoption of Connected Autonomous Vehicles (CAVs) in the near future. Despite some…

Vehicular communication networks are rapidly emerging as vehicles become smarter. However, these networks are increasingly susceptible to various attacks. The situation is exacerbated by the rise in automated vehicles complicates,…

Over the past few years, traffic congestion has continuously plagued the nation's transportation system creating several negative impacts including longer travel times, increased pollution rates, and higher collision risks. To overcome…

This paper discusses the need for individualizing safety systems and proposes an approach including the Real-Time estimation of the distribution of brake response times for an individual driver. While maintaining high level of safety, the…

This paper presents a survey of state-of-the-art trust models for Vehicular Ad Hoc Networks (VANETs). Trust management plays an essential role in isolating malicious insider attacks in VANETs which traditional security approaches fail to…

Connected and Autonomous vehicles stand to drastically improve the safety and efficiency of the transportation system in the near future while also reducing pollution. These systems leverage communication to coordinate among vehicles and…

Connected and Autonomous Vehicles (CAVs) rely on Vehicular Adhoc Networks with wireless communication between vehicles and roadside infrastructure to support safe operation. However, cybersecurity attacks pose a threat to VANETs and the…

Cooperative driving is an emerging paradigm to enhance the safety and efficiency of autonomous vehicles. To ensure successful cooperation, road users must reach a consensus for making collective decisions, while recording vehicular data to…

Vehicular Ad-hoc Networks (VANET) is a derived subclass of Mobile Ad-hoc Networks (MANET) with vehicles as mobile nodes. VANET facilitate vehicles to share safety and non-safety information through messages. Safety information includes road…

In vehicular networks, vehicles exchange messages with each other as well as infrastructure to prevent accidents or enhance driver's and passenger's experience. In this paper, we propose a grid-based multichannel access scheme to enhance…

Privacy-preserving machine learning (PPML) enables clients to collaboratively train deep learning models without sharing private datasets, but faces privacy leakage risks due to gradient leakage attacks. Prevailing methods leverage secure…

Smart vehicles produce large amounts of data, much of which is sensitive and at risk of privacy breaches. As attackers increasingly exploit anonymised metadata within these datasets to profile drivers, it's important to find solutions that…

Searching for available parking spaces is a major problem for drivers especially in big crowded cities, causing traffic congestion and air pollution, and wasting drivers' time. Smart parking systems are a novel solution to enable drivers to…