Group-Grained Data Search and Sharing with Privacy Protection for Vehicular Social Networks
Zhou, Rang Li, Dongfen Li, Wanpeng Zhang, Xiaojun Du, Xiaojiang Guizani, Mohsen
Zhou, Rang
Li, Dongfen
Li, Wanpeng
Zhang, Xiaojun
Du, Xiaojiang
Guizani, Mohsen
Supervisor
Department
Machine Learning
Embargo End Date
Type
Journal article
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Vehicular social networks (VSNs) play a crucial role in intelligent transportation systems, offering high-quality data management services that enhance various aspects of daily life. Due to their convenience, VSN systems, equipped with advanced data search and sharing capabilities, are increasingly integrated into modern vehicles. While earlier VSNs focused on securing data communication between users, the transmission of sensitive vehicle and traffic data, like road conditions and vehicle trajectories, has raised privacy concerns and the risk of data leakage, which could harm vehicle owners' interests. Historically, these systems focused primarily on securing data communication between VSN users. However, the transmission of sensitive vehicle and traffic data, such as road conditions and vehicle trajectory information, has raised concerns about data privacy and the potential risks of data leakage, which could compromise the interests of vehicle owners. To address these challenges, we propose a novel group-grained data search and sharing scheme for VSN systems. Unlike traditional attribute-based encryption methods used in data management, our approach introduces a group-grained model that enables fine-grained control over search rights and data-sharing isolation, ensuring enhanced data privacy. Additionally, to reduce the computational burden on these Internet of Thing (IoT) devices, our scheme ensures constant-sized keyword index generation, data index generation, trapdoor creation, and decryption processes. We evaluate the efficiency of our construction and compare it with similar constructions. The results demonstrate that our construction is well suited for resource-constrained IoT devices in VSN systems.
Citation
R. Zhou, D. Li, W. Li, X. Zhang, X. Du, and M. Guizani, “Group-Grained Data Search and Sharing With Privacy Protection for Vehicular Social Networks,” IEEE Internet Things J, 2024, doi: 10.1109/JIOT.2024.3523910.
Source
IEEE Internet of Things Journal
Conference
Keywords
Data sharing, group-grained, lightweight trap-door, multiowner setting, vehicular social network (VSN)
Subjects
Source
Publisher
IEEE