Title: Blockchain-based privacy-preserving technology to secure shared data in vehicular communication

Authors: Omessaad Slama; Walid Dhifallah; Salah Zidi; Jaime Lloret; Bechir Alaya; Mounira Tarhouni

Addresses: Hatem Bettaher Laboratory, IRESCOMATH, University of Gabes, Gabes 6029, Tunisia ' Hatem Bettaher Laboratory, IRESCOMATH, University of Gabes, Gabes 6029, Tunisia ' Hatem Bettaher Laboratory, IRESCOMATH, University of Gabes, Gabes 6029, Tunisia ' Integrated Management Coastal Research Institute, Universitat Politècnica de València, Spain ' Hatem Bettaher Laboratory, IResCoMath, University of Gabes, Gabes 6029, Tunisia; Department of Management Information Systems and Production Management, College of Business and Economics, Qassim University, Buraydah 51452, Saudi Arabia ' Hatem Bettaher Laboratory, IRESCOMATH, University of Gabes, Gabes 6029, Tunisia

Abstract: In vehicular ad-hoc networks (VANETs), securely exchanging sensitive data like misbehaviour detection models faces significant security and privacy hurdles. Our solution, machine learning model blockchain-based privacy-preserving (MBPP), combines Blockchain technology and advanced cryptography to tackle this. MBPP ensures data confidentiality and integrity while improving detection model reliability. It involves securely storing ML models on the blockchain using cryptographic hash functions. Our study meticulously evaluates transactional time and computational costs, vital for smooth blockchain transactions. This research not only presents a conceptual framework for blockchain use in VANETs but also offers insights into managing transactions via smart contracts, addressing VANETs' security and privacy challenges effectively.

Keywords: blockchain; smart contract; computational costs; artificial intelligence algorithms; VANETs; vehicular ad-hoc networks; data sharing; privacy preservation; cryptography hash function.

DOI: 10.1504/IJCNDS.2025.145910

International Journal of Communication Networks and Distributed Systems, 2025 Vol.31 No.3, pp.346 - 371

Received: 13 Nov 2023
Accepted: 23 Jan 2024
Published online: 30 Apr 2025 *

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