Open Access

ARTICLE

A Blockchain-Assisted Distributed Edge Intelligence for Privacy-Preserving Vehicular Networks

by Muhammad Firdaus¹, Harashta Tatimma Larasati², Kyung-Hyune Rhee^3,*

1 Department of Artificial Intelligence Convergence, Pukyong National University, Busan, 48513, Korea
2 School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, 40132, Indonesia
3 College of Information Technology and Convergence, Division of Computer Engineering and AI, Pukyong National University, Busan, 48513, Korea

* Corresponding Author: Kyung-Hyune Rhee. Email:

(This article belongs to the Special Issue: Advances in Information Security Application)

Computers, Materials & Continua 2023, 76(3), 2959-2978. https://doi.org/10.32604/cmc.2023.039487

Received 01 February 2023; Accepted 20 April 2023; Issue published 08 October 2023

Abstract

The enormous volume of heterogeneous data from various smart device-based applications has growingly increased a deeply interlaced cyber-physical system. In order to deliver smart cloud services that require low latency with strong computational processing capabilities, the Edge Intelligence System (EIS) idea is now being employed, which takes advantage of Artificial Intelligence (AI) and Edge Computing Technology (ECT). Thus, EIS presents a potential approach to enforcing future Intelligent Transportation Systems (ITS), particularly within a context of a Vehicular Network (VNets). However, the current EIS framework meets some issues and is conceivably vulnerable to multiple adversarial attacks because the central aggregator server handles the entire system orchestration. Hence, this paper introduces the concept of distributed edge intelligence, combining the advantages of Federated Learning (FL), Differential Privacy (DP), and blockchain to address the issues raised earlier. By performing decentralized data management and storing transactions in immutable distributed ledger networks, the blockchain-assisted FL method improves user privacy and boosts traffic prediction accuracy. Additionally, DP is utilized in defending the user’s private data from various threats and is given the authority to bolster the confidentiality of data-sharing transactions. Our model has been deployed in two strategies: First, DP-based FL to strengthen user privacy by masking the intermediate data during model uploading. Second, blockchain-based FL to effectively construct secure and decentralized traffic management in vehicular networks. The simulation results demonstrated that our framework yields several benefits for VNets privacy protection by forming a distributed EIS with privacy budget (ε) of 4.03, 1.18, and 0.522, achieving model accuracy of 95.8%, 93.78%, and 89.31%, respectively.

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Keywords

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