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BRIP: Towards a privacy-preserving, trustworthy, and transparent ride-sharing system with semantic matching powered by blockchain and IPFS

计算机科学 块链 可信赖性 计算机安全 匹配(统计) 互联网隐私 数学 统计
作者
Nesma Mahmoud,Hatem Abdelkader,Asmaa Aly
出处
期刊:Journal of Network and Computer Applications [Elsevier]
卷期号:226: 103870-103870 被引量:1
标识
DOI:10.1016/j.jnca.2024.103870
摘要

This paper introduces a decentralized ride-sharing system called BRIP that utilizes blockchain technology. BRIP addresses various issues commonly associated with centralized ride-sharing such as single points of failure, privacy violations, lack of security and transparency, and distributed denial of service (DDoS) attacks, by utilizing the blockchain. It also integrates the Interplanetary File System (IPFS) with blockchain to move ride-sharing data from the blockchain to the IPFS and retaining only a compact IPFS hash for storage. This integration resolves the scalability challenges associated with storing and handling large volumes of ride-sharing data on the blockchain. However, the use of blockchain technology alone, even with IPFS integration, does not guarantee complete solutions for privacy, security, transparency, and trust. Therefore, to ensure security and privacy in BRIP, ride-sharing data is encrypted before being stored in the IPFS and blockchain, as well as processing these data. This paper also implements pseudonyms, unlinkability, and encryption techniques to protect users' privacy. To establish trust among users and ensure transparency of all ride-sharing transactions, we proposed an enhanced methodology by leveraging the capabilities of blockchain technology and smart contracts. This methodology is also designed in a way that helps in privacy-preservation, trust, and transparency. Furthermore, the trade-off between privacy-preservation is still a challenge in blockchain-based ride-sharing systems, which is resolved through semantic matching. Therefore, the paper proposes two semantic models, COR2Vec and customized Time2Vec, to accurately and semantically match, spatially and temporarily respectively, users while preserving their privacy. Finally, experimental results demonstrate the effectiveness and efficiency of the proposed BRIP system.
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