Privacy‐preserving blockchain‐based authentication and trust management in VANETs

Abstract

Vehicular Ad Hoc Networks (VANETs) are characterized by high mobility of nodes and volatility, which make privacy, trust management, and security challenging issues in VANETs' design. In such networks, data can be exposed to a variety of attacks, the most dangerous is false information dissemination, which threatens the safety and efficiency of transportation systems. False emergency messages can be injected by inside attackers to announce fake incidents such as traffic accidents, resulting in a false information attack. As the data in VANET is based on events, any trust mechanism must first identify the true events. To address these security challenges, a blockchain-based authentication scheme and trust management model are proposed for VANETs. Using the authentication scheme, vehicles are enabled to send messages anonymously to the roadside units (RSUs) and the identity privacy of vehicles is protected. Besides, the proposed trust management model is designed to detect and deal with false information by evaluating the trustworthiness of vehicles and data. Using the trust model, when vehicles report an incident to the nearest RSU, the RSU is able to verify whether or not the incident took place. This mechanism ensures that RSUs send only verified event notifications. Finally, RSUs participate in updating the trust values of vehicles and store these values in the blockchain. The efficiency of the proposed authentication scheme is validated through analysis while the trust model is validated through simulations. The results obtained show that the proposed authentication scheme and the trust model provide better performance than other state-of-the-art models where malicious vehicles can be identified efficiently and RSUs are enabled to broadcast only legitimate events.

SSE_CIB: Secure storage and exchange of copyrighted images using blockchain

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Towards Risk-Free Trustworthy Artificial Intelligence: Significance and Requirements

“…For both centralised and decentralised data processing, training data must be preprocessed or reconstructed to secure and protect any sensitive data, but this may reduce system accuracy. Terefore, robust privacy-preserving computation is necessary to compensate for the trade-of between system accuracy and data privacy [223,224]. Tese issues must be addressed using a fair system design.…”

Event Message Clustering Algorithm for Selection of Majority Message in VANETs

“…The trust value of a vehicular node is calculated based on direct interactions between vehicular nodes and RSUs. The value is calculated as the ratio of true event reports from a vehicle to the total number of event reports from that vehicle in a given period of time [3], [29]. If x i is the number of true messages from vehicle v i , and y i is the total number of messages it has generated up to a specific time, the trust value is calculated with (2).…”