Security and Privacy for Smart Transportation Management using Big Data Analytics

Govindasamy R.; Shanmugapriya N.; Gopi R.

doi:10.52756/ijerr.2024.v40spl.008

Govindasamy R. Department of Computer Science & Engineering, School of Engineering & Technology, Dhanalakshmi Srinivasan University, Trichy - 621112, Tamil Nadu, India https://orcid.org/0009-0009-3429-5648
Shanmugapriya N. Department of Computer Science & Engineering, School of Engineering & Technology, Dhanalakshmi Srinivasan University, Trichy - 621112, Tamil Nadu, India https://orcid.org/0000-0002-4620-1807
Gopi R. Department of Computer Science & Engineering, Dhanalakshmi Srinivasan Engineering College, Perambalur - 621212, Tamil Nadu, India https://orcid.org/0000-0003-4957-1843

DOI: https://doi.org/10.52756/ijerr.2024.v40spl.008

Keywords: Data encryption, security, privacy, big data, smart transportation

Abstract

Security and privacy are vital aspects of smart transportation management with big data analytics because they assure the security of sensitive information, prevent unwanted access to essential systems, and retain public trust in the safety and dependability of the transportation infrastructure. Protecting data from cyber threats, ensuring secure communication and data transmission, protecting passengers' personal information and addressing privacy concerns related to data collection and usage to maintain transparency and accountability in data handling practices are all obstacles to smart transportation management using big data analytics. This paper proposes a Secure Data Encryption Control based Big Data Framework (SDEC-BDF) to strike a middle ground between data analytics and privacy protection, establishing the way for more private and secure transportation systems that benefit everyone involved. The intention of this approach is to offer strong security while simultaneously safeguarding people's privacy. The method has many potential uses in the Intelligent transportation sector (ITS), including traffic control, passenger security, fleet management, preventative maintenance, and road network design. It ensures privacy and security while facilitating effective data analysis. Furthermore, it protects the public confidence in the security and dependability of the transportation system, protects sensitive passenger data, and stops hackers from breaking into vital systems. The simulation analysis is conducted on the assumption that the system can maximize its security, privacy, and efficiency to create a more trustworthy transportation network.

References

Al-Turjman, F., Zahmatkesh, H., & Shahroze, R. (2022). An overview of security and privacy in smart cities’ IoT communications. Transactions on Emerging Telecommunications Technologies, 33(3), e3677. https://doi.org/10.1002/ett.3677

Arooj, A., Farooq, M. S., Akram, A., Iqbal, R., Sharma, A., & Dhiman, G. (2022). Big Data Processing and Analysis in Internet of Vehicles: Architecture, Taxonomy, and Open Research Challenges. In Archives of Computational Methods in Engineering, 29(2), 793–829. https://doi.org/10.1007/s11831-021-09590-x

Atitallah, S. Ben, Driss, M., Boulila, W., & Ghezala, H. Ben. (2020). Leveraging Deep Learning and IoT big data analytics to support the smart cities development: Review and future directions. In Computer Science Review, 38. https://doi.org/10.1016/j.cosrev.2020.100303

Bhattarai, B. P., Paudyal, S., Luo, Y., Mohanpurkar, M., Cheung, K., Tonkoski, R., Hovsapian, R., Myers, K. S., Zhang, R., Zhao, P., Manic, M., Zhang, S., & Zhang, X. (2019). Big data analytics in smart grids: State-of-theart, challenges, opportunities, and future directions. In IET Smart Grid 2(2), 141-154. https://doi.org/10.1049/iet-stg.2018.0261

Chanal, P. M., & Kakkasageri, M. S. (2020). Security and Privacy in IoT: A Survey. In Wireless Personal Communications, 115(2), 1667–1693. https://doi.org/10.1007/s11277-020-07649-9

Chang, V. (2021). An ethical framework for big data and smart cities. Technological Forecasting and Social Change, 165. https://doi.org/10.1016/j.techfore.2020.120559

Ding, W., Jing, X., Yan, Z., & Yang, L. T. (2019). A survey on data fusion in internet of things: Towards secure and privacy-preserving fusion. Information Fusion, 51, 129-144. https://doi.org/10.1016/j.inffus.2018.12.001

Ding, Y., Jin, M., Li, S., & Feng, D. (2021). Smart logistics based on the internet of things technology: an overview. International Journal of Logistics Research and Applications, 24(4), 323–345. https://doi.org/10.1080/13675567.2020.1757053

Garg, S., Singh, A., Kaur, K., Aujla, G. S., Batra, S., Kumar, N., & Obaidat, M. S. (2019). Edge Computing-Based Security Framework for Big Data Analytics in VANETs. IEEE Network, 33(2), 72-81. https://doi.org/10.1109/MNET.2019.1800239

Gifty, R., Bharathi, R., & Krishnakumar, P. (2019). Privacy and security of big data in cyber physical systems using Weibull distribution-based intrusion detection. Neural Computing and Applications, 31, 23–34. https://doi.org/10.1007/s00521-018-3635-6

Gupta, M., Abdelsalam, M., Khorsandroo, S., & Mittal, S. (2020). Security and Privacy in Smart Farming: Challenges and Opportunities. IEEE Access, 8, 34564-34584. https://doi.org/10.1109/ACCESS.2020.2975142

Hahn, D., Munir, A., & Behzadan, V. (2021). Security and privacy issues in intelligent transportation systems: Classification and challenges. IEEE Intelligent Transportation Systems Magazine, 13(1), 181-196. https://doi.org/10.1109/MITS.2019.2898973

Kumar, A., Dutta, S., & Pranav, P. (2023a). Prevention of VM Timing side-channel attack in a cloud environment using randomized timing approach in AES – 128. Int. J. Exp. Res. Rev., 31(Spl Volume), 131-140. https://doi.org/10.52756/10.52756/ijerr.2023.v31spl.013

Kumar, A., Dutta, S., & Pranav, P. (2023b). Supervised learning for Attack Detection in Cloud. Int. J. Exp. Res. Rev., 31(Spl Volume), 74-84. https://doi.org/10.52756/10.52756/ijerr.2023.v31spl.008

Li, W., Chai, Y., Khan, F., Jan, S. R. U., Verma, S., Menon, V. G., Kavita, & Li, X. (2021). A Comprehensive Survey on Machine Learning-Based Big Data Analytics for IoT-Enabled Smart Healthcare System. Mobile Networks and Applications, 26(1), 234–252. https://doi.org/10.1007/s11036-020-01700-6

Liu, C., Feng, Y., Lin, D., Wu, L., & Guo, M. (2020). Iot based laundry services: an application of big data analytics, intelligent logistics management, and machine learning techniques. International Journal of Production Research, 58(17), 5113–5131. https://doi.org/10.1080/00207543.2019.1677961

Liu, Y., Yang, C., & Sun, Q. (2021). Thresholds Based Image Extraction Schemes in Big Data Environment in Intelligent Traffic Management. IEEE Transactions on Intelligent Transportation Systems, 22(7), 3952-3960. https://doi.org/10.1109/TITS.2020.2994386

Musa, A. A., Malami, S. I., Alanazi, F., Ounaies, W., Alshammari, M., & Haruna, S. I. (2023). Sustainable Traffic Management for Smart Cities Using Internet-of-Things-Oriented Intelligent Transportation Systems (ITS): Challenges and Recommendations. Sustainability, 15(13), 9859. https://doi.org/10.3390/su15139859

Mohanta, B. K., Jena, D., Ramasubbareddy, S., Daneshmand, M., & Gandomi, A. H. (2021). Addressing Security and Privacy Issues of IoT Using Blockchain Technology. IEEE Internet of Things Journal, 8(2), 881-888. https://doi.org/10.1109/JIOT.2020.3008906

Neilson, A., Indratmo, Daniel, B., & Tjandra, S. (2019). Systematic Review of the Literature on Big Data in the Transportation Domain: Concepts and Applications. In Big Data Research, 17, 35-44. https://doi.org/10.1016/j.bdr.2019.03.001

Rajak, R., Choudhary, A., & Sajid, M. (2023). Load balancing techniques in cloud platform: A systematic study. Int. J. Exp. Res. Rev., 30, 15-24. https://doi.org/10.52756/ijerr.2023.v30.002

Rajbhandari, S., & Sharma, R. (2024). Using Big Data and the Internet of Things to Optimize Public Transport Efficiency Across Major Cities in India. Journal of Intelligent Connectivity and Emerging Technologies, 9(1), 13-24.

Samadder, M., Barman, A., & Roy, A. (2023). Examining a generic streaming architecture for smart manufacturing’s Big data processing in Anomaly detection: A review and a proposal. Int. J. Exp. Res. Rev., 30, 219-227. https://doi.org/10.52756/ijerr.2023.v30.019

Singh, S. K., Rathore, S., & Park, J. H. (2020). BlockIoTIntelligence: A Blockchain-enabled Intelligent IoT Architecture with Artificial Intelligence. Future Generation Computer Systems, 110, 721-743. https://doi.org/10.1016/j.future.2019.09.002

Soomro, K., Bhutta, M. N. M., Khan, Z., & Tahir, M. A. (2019). Smart city big data analytics: An advanced review. In Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(5). https://doi.org/10.1002/widm.1319

Tariq, N., Asim, M., Al-Obeidat, F., Farooqi, M. Z., Baker, T., Hammoudeh, M., & Ghafir, I. (2019). The security of big data in fog-enabled IOT applications including blockchain: A survey. Sensors (Switzerland), 19(8), 1788. https://doi.org/10.3390/s19081788

Wan, J., Li, J., Imran, M., & Li, D. (2019). A blockchain-based solution for enhancing security and privacy in smart factory. In IEEE Transactions on Industrial Informatics, 15(6), 3652-3660. https://doi.org/10.1109/TII.2019.2894573

Zeadally, S., Siddiqui, F., Baig, Z., & Ibrahim, A. (2020). Smart healthcare: Challenges and potential solutions using internet of things (IoT) and big data analytics. PSU Research Review, 4(2), 149-168. https://doi.org/10.1108/PRR-08-2019-0027