SBM-SA: A Safety Beacon Message Separation Algorithm for Privacy Protection in Internet of Vehicles
Main Article Content
Keywords
Internet of Vehicles (IoV), privacy protection, safety beacon message (SBM)
Abstract
A safety beacon message (SBM) plays a pivotal role in the Internet of Vehicles (IoV), broadcasting crucial events and road conditions to nearby vehicles. Given the sensitive nature of the data, such as vehicle identity and location, ensuring privacy is paramount. The significance of this research lies in addressing the pressing need for comprehensive privacy protection in the IoV, especially as most existing schemes focus on safeguarding either vehicle identity or location data during exchanges with core servers. The primary objective of this article is to introduce an SBM separation algorithm, termed SBM-SA, designed to holistically protect both identity and location data. Utilising correlation analysis, the SBM-SA stands as an innovative anonymisation privacy algorithm. Through a simulated IoV environment, the accuracy and efficacy of an SBM-SA are meticulously analysed and juxtaposed against prevailing privacy protection schemes. The findings underscore the SBM-SA’s potential to significantly enhance privacy measures in the IoV. Implications of this research extend to shaping future privacy protection strategies of the SBM, emphasising the need for holistic and robust solutions in an increasingly interconnected vehicular landscape.
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References
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Kietzmann, J. H. (2017). Crowdsourcing: A revised definition and introduction to new research. Business horizons, 60(2), 151–153. https://doi.org/10.1016/j.bushor.2016.10.001
Kim, J. W., Edemacu, K., & Jang, B. (2022). Privacy-preserving mechanisms for location privacy in mobile crowdsensing: A survey. Journal of Network and Computer Applications, 200, 103315. https://doi.org/10.1016/j.jnca.2021.103315
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Lin, H., Garg, S., Hu, J., Kaddoum, G., Peng, M., & Hossain, M. S. (2020). Blockchain and deep reinforcement learning empowered spatial crowdsourcing in software-defined internet of vehicles. IEEE Transactions on Intelligent Transportation Systems, 22(6), 3755–3764. https://doi.org/10.1109/TITS.2020.3025247
Liu, Y., Wang, H., Peng, M., Guan, J., & Wang, Y. (2020). An incentive mechanism for privacy-preserving crowdsensing via deep reinforcement learning. IEEE Internet of Things Journal, 8(10), 8616–8631. https://doi.org/10.1109/JIOT.2020.3047105
Liu, J., Peng, C., Sun, R., Liu, L., Zhang, N., Dustdar, S., & Leung, V. C. (2023). CPAHP: Conditional privacy-preserving authentication scheme with hierarchical pseudonym for 5G-enabled IoV. IEEE Transactions on Vehicular Technology, 72(7), 8929–8940. https://doi.org/10.1109/TVT.2023.3246466
Liu, K., Li, H., Chen, X., Liao, D., Peng, L., & Yurui, L. (2022, October). A privacy protection solution based on data aggregation and batch authentication. In Proceedings of the 5th International ACM Mobicom Workshop on Drone Assisted Wireless Communications for 5G and Beyond, 85–90. https://doi.org/10.1145/3555661.3560869
Liu, T., Zhu, Y., Yang, Y., & Ye, F. (2016, December). Incentive design for air pollution monitoring based on compressive crowdsensing. In 2016 IEEE Global Communications Conference (GLOBECOM) (pp. 1-6). IEEE. https://doi.org/10.1109/GLOCOM.2016.7841892
Mei, Q., Gül, M., & Shirzad-Ghaleroudkhani, N. (2020). Towards smart cities: Crowdsensing-based monitoring of transportation infrastructure using in-traffic vehicles. Journal of Civil Structural Health Monitoring, 10(4), 653–665. https://doi.org/10.1007/s13349-020-00411-6
Memon, I., Chen, L., Arain, Q. A., Memon, H., & Chen, G. (2018). Pseudonym changing strategy with multiple mix zones for trajectory privacy protection in road networks. International Journal of Communication Systems, 31(1), e3437. https://doi.org/10.1002/dac.3437
Metlo, S., Memon, M. G., Shaikh, F. K., Teevno, M. A., & Talpur, A. (2019). Crowdsource based vehicle tracking system. Wireless Personal Communications, 106(4), 2387–2405. https://doi.org/10.1007/s11277-019-06323-z
Misra, A., Gooze, A., Watkins, K., Asad, M., & Le Dantec, C. A. (2014). Crowdsourcing and its application to transportation data collection and management. Transportation Research Record, 2414(1), 1–8. https://doi.org/10.3141/2414-01
Nandy, T., Idris, M. Y. I., Noor, R. M., Wahab, A. W. A., Bhattacharyya, S., Kolandaisamy, R., & Yahuza, M. (2021). A secure, privacy-preserving, and lightweight Authentication scheme for VANETs. IEEE Sensors Journal, 21(18), 20998–21011. https://doi.org/10.1109/JSEN.2021.3097172
Qian, Y., Ma, Y., Chen, J., Wu, D., Tian, D., & Hwang, K. (2021). Optimal location privacy preserving and service quality guaranteed task allocation in vehicle-based crowdsensing networks. IEEE Transactions on Intelligent Transportation Systems, 22(7), 4367–4375. https://doi.org/10.1109/TITS.2021.3086837
Qureshi, K., & Abdullah, H. (2013). A survey on intelligent transportation systems. Middle East Journal of Scientific Research, 15, 629–642. https://doi.org/10.5829/idosi.mejsr.2013.15.5.11215
Sadiah, S., & Nakanishi, T. (2022). An efficient anonymous reputation system for crowdsensing. Journal of Information Processing, 30, 694–705. https://doi.org/10.2197/ipsjjip.30.694
Song, L., Sun, G., Yu, H., Du, X., & Guizani, M. (2020). Fbia: A fog-based identity authentication scheme for privacy preservation in internet of vehicles. IEEE Transactions on Vehicular Technology, 69(5), 5403–5415. https://doi.org/10.1109/TVT.2020.2977829
Wang, T., Xu, L., Zhang, M., Zhang, H., & Zhang, G. (2022). A new privacy protection approach based on k-anonymity for location-based cloud services. Journal of Circuits, Systems and Computers, 31(05), 2250083. https://doi.org/10.1142/S0218126622500839
Wang, D., Huang, C., Shen, X., & Xiong, N. (2020). A general location-authentication based secure participant recruitment scheme for vehicular crowdsensing. Computer Networks, 171, 107152. https://doi.org/10.1016/j.comnet.2020.107152
Wang, X., Zhang, J., Tian, X., Gan, X., Guan, Y., & Wang, X. (2017). Crowdsensing-based consensus incident report for road traffic acquisition. IEEE Transactions on Intelligent Transportation Systems, 19(8), 2536–2547. https://doi.org/10.1109/TITS.2017.2750169
Wei, H., Wang, J., Jian, M., Mei, S., & Huang, M. (2021, April). Steer-by-Wire Control System Based on Carsim and Simulink. In 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), 1–5. IEEE. https://doi.org/10.1109/IEMTRONICS52119.2021.9422502
Wu, Z., Wang, R., Li, Q., Lian, X., Xu, G., Chen, E., & Liu, X. (2020). A location privacy-preserving system based on query range cover-up or location-based services. IEEE Transactions on Vehicular Technology, 69(5), 5244–5254. https://doi.org/10.1109/TVT.2020.2981633
Xiong, J., Ma, R., Chen, L., Tian, Y., Li, Q., Liu, X., & Yao, Z. (2019). A personalized privacy protection framework for mobile crowdsensing in IIoT. IEEE Transactions on Industrial Informatics, 16(6), 4231–4241. https://doi.org/10.1109/TII.2019.2948068
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Published
Dec 15, 2023
Article Details
How to Cite
Jiang, Z., Chua, F.-F., & Lim, A. H.-L. (2023). SBM-SA: A Safety Beacon Message Separation Algorithm for Privacy Protection in Internet of Vehicles. Journal of Telecommunications and the Digital Economy, 11(4), 66–93. https://doi.org/10.18080/jtde.v11n4.767
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Telecommunications
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