Blockchain for Secure and Efficient Data Sharing in Vehicular Edge Computing and Networks
The drastically increasing volume and the growing trend on the types of data have brought in the possibility of real- izing advanced applications such as enhanced driving safety, and have enriched existing vehicular services through data sharing among vehicles and data analysis. Due to limited resources with vehicles, vehicular edge computing and networks (VECONs) i.e., the integration of mobile edge computing and vehicular networks, can provide powerful computing and massive storage resources. However, road side units that primarily presume the role of vehic- ular edge computing servers cannot be fully trusted, which may lead to serious security and privacy challenges for such inte- grated platforms despite their promising potential and benefits. We exploit consortium blockchain and smart contract technolo- gies to achieve secure data storage and sharing in vehicular edge networks. These technologies efficiently prevent data sharing without authorization. In addition, we propose a reputation-based data sharing scheme to ensure high-quality data sharing among vehicles. A three-weight subjective logic model is utilized for precisely managing reputation of the vehicles. Numerical results based on a real dataset show that our schemes achieve reasonable efficiency and high-level of security for data sharing in VECONs.
Code (0)
등록된 구현이 없습니다.
Tasks
Edge-computingSimilar Papers 제목 키워드 기반
BCGS: Blockchain-assisted privacy-preserving cross-domain authentication for VANETs
Vehicular Ad-Hoc Networks (VANETs) have significantly enhanced driving safety and comfort by leveraging vehicular wireless communication technology. Secure authentication among vehicles in VANETs is an important requirem…
Privacy PreservingDeep Reinforcement Learning and Permissioned Blockchain for Content Caching in Vehicular Edge Computing and Networks
Vehicular Edge Computing (VEC) is a promising paradigm to enable huge amount of data and multimedia content to be cached in proximity to vehicles. However, high mobility of vehicles and dynamic wireless channel condition…
Deep Reinforcement LearningEdge-computingReinforcement Learning (RL)Blockchain-Enabled Federated Learning Approach for Vehicular Networks
Data from interconnected vehicles may contain sensitive information such as location, driving behavior, personal identifiers, etc. Without adequate safeguards, sharing this data jeopardizes data privacy and system securi…
Federated LearningA Drone-Aided Blockchain-Based Smart Vehicular Network
The staggering growth of the number of vehicles worldwide has become a critical challenge resulting in tragic incidents, environment pollution, congestion, etc. Therefore, one of the promising approaches is to design a s…
Secure Architectures Implementing Trusted Coalitions for Blockchained Distributed Learning (TCLearn)
Distributed learning across a coalition of organizations allows the members of the coalition to train and share a model without sharing the data used to optimize this model. In this paper, we propose new secure architect…
Distributed Optimization