International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Protecting Privacy of User Data in Intelligent Transportation Systems

  • Yazed Alsaawy (Department of Computer Science Faculty of Computer and Information Systems Islamic University of Madinah) ;
  • Ahmad Alkhodre (Department of Computer Science Faculty of Computer and Information Systems Islamic University of Madinah) ;
  • Adnan Abi Sen (Department of Computer Science Faculty of Computer and Information Systems Islamic University of Madinah)
  • Received : 2023.05.05
  • Published : 2023.05.30
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Abstract

The intelligent transportation system has made a huge leap in the level of human services, which has had a positive impact on the quality of life of users. On the other hand, these services are becoming a new source of risk due to the use of data collected from vehicles, on which intelligent systems rely to create automatic contextual adaptation. Most of the popular privacy protection methods, such as Dummy and obfuscation, cannot be used with many services because of their impact on the accuracy of the service provided itself, they depend on changing the number of vehicles or their physical locations. This research presents a new approach based on the shuffling Nicknames of vehicles. It fully maintains the quality of the service and prevents tracking users permanently, penetrating their privacy, revealing their whereabouts, or discovering additional details about the nature of their behavior and movements. Our approach is based on creating a central Nicknames Pool in the cloud as well as distributed subpools in fog nodes to avoid intelligent delays and overloading of the central architecture. Finally, we will prove by simulation and discussion by examples the superiority of the proposed approach and its ability to adapt to new services and provide an effective level of protection. In the comparison, we will rely on the wellknown privacy criteria: Entropy, Ubiquity, and Performance.

Keywords

Acknowledgement

The Deanship of Scientific Research, Islamic University of Madinah, Saudi Arabia, funded this research under Tamayuz research grant number 2/710.

References

  1. Mahmud, K., Gope, K., & Chowdhury, S. M. R. (2012). Possible causes & solutions of traffic jam and their impact on the economy of Dhaka City. J. Mgmt. & Sustainability, 2, 112.
  2. Petrovska, N., & Stevanovic, A. (2015, September). Traffic congestion analysis visualization tool. In 2015 IEEE 18th International Conference on Intelligent Transportation Systems (pp. 1489-1494). IEEE.
  3. Isa, M. N., & Siyan, P. (2016). Analyzing factors responsible for road traffic accidents along Kano-Kaduna-Abuja Dual Carriageway Nigeria. Journal of Economics and Sustainable Development, 7(12).
  4. Farda, M., & Balijepalli, C. (2018). Exploring the effectiveness of demand management policy in reducing traffic congestion and environmental pollution: Car-free day and odd-even plate measures for Bandung city in Indonesia. Case Studies on Transport Policy, 6(4), 577-590. https://doi.org/10.1016/j.cstp.2018.07.008
  5. Patel, P., Narmawala, Z., & Thakkar, A. (2019). A survey on intelligent transportation system using internet of things. Emerging Research in Computing, Information, Communication and Applications, 231-240.
  6. Alam, T. (2020). Cloud Computing and its role in the Information Technology. IAIC Transactions on Sustainable Digital Innovation (ITSDI), 1(2), 108-115. https://doi.org/10.34306/itsdi.v1i2.103
  7. Atlam, H. F., Walters, R. J., & Wills, G. B. (2018). Fog computing and the internet of things: A review. Big data and cognitive computing, 2(2), 10.
  8. Kandris, D., Nakas, C., Vomvas, D., & Koulouras, G. (2020). Applications of wireless sensor networks: an up-to-date survey. Applied System Innovation, 3(1), 14.
  9. Ibrahim, A. A. A., Nisar, K., Hzou, Y. K., & Welch, I. (2019, October). Review and analyzing RFID technology tags and applications. In 2019 IEEE 13th International Conference on Application of Information and Communication Technologies (AICT) (pp. 1-4). IEEE.
  10. Mandhare, P. A., Kharat, V., & Patil, C. Y. (2018). Intelligent road traffic control system for traffic congestion a perspective. International Journal of Computer Sciences and Engineering, 6(07), 2018.
  11. Jabbarpour, M. R., Zarrabi, H., Khokhar, R. H., Shamshirband, S., & Choo, K. K. R. (2018). Applications of computational intelligence in vehicle traffic congestion problem: a survey. Soft Computing, 22(7), 2299-2320. https://doi.org/10.1007/s00500-017-2492-z
  12. Nguyen, M. Q., Pham, T. T. X., & Phan, T. T. H. (2019). Traffic Congestion-A Prominent Problem in Vietnam Current Situation and Solutions.
  13. Pop, M. D. (2018). Traffic lights management using optimization tool. Procedia-social and behavioral sciences, 238, 323-330. https://doi.org/10.1016/j.sbspro.2018.04.008
  14. Alsaawy, Y., Alkhodre, A., Abi Sen, A., Alshanqiti, A., Bhat, W. A., & Bahbouh, N. M. (2022). A Comprehensive and Effective Framework for Traffic Congestion Problem Based on the Integration of IoT and Data Analytics. Applied Sciences, 12(4), 2043.
  15. Cao, Z., Ceder, A. A., & Zhang, S. (2019). Real-time schedule adjustments for autonomous public transport vehicles. Transportation Research Part C: Emerging Technologies, 109, 60-78. https://doi.org/10.1016/j.trc.2019.10.004
  16. Tokody, D., Mezei, I. J., & Schuster, G. (2017). An overview of autonomous intelligent vehicle systems. Vehicle and Automotive Engineering, 287-307.
  17. Elsagheer Mohamed, S. A., & AlShalfan, K. A. (2021). Intelligent traffic management system based on the internet of vehicles (IoV). Journal of advanced transportation, 2021.
  18. Alharbi, A., Halikias, G., Sen, A. A. A., & Yamin, M. (2021). A framework for dynamic smart traffic light management system. International Journal of Information Technology, 13(5), 1769-1776. https://doi.org/10.1007/s41870-021-00755-2
  19. Lin, T., Rivano, H., & Le Mouel, F. (2017). A survey of smart parking solutions. IEEE Transactions on Intelligent Transportation Systems, 18(12), 3229-3253. https://doi.org/10.1109/TITS.2017.2685143
  20. Alharbi, A., Halikias, G., Yamin, M., Sen, A., & Ahmed, A. (2021). Web-based framework for smart parking system. International Journal of Information Technology, 13(4), 1495-1502. https://doi.org/10.1007/s41870-021-00725-8
  21. Lv, Y., Chen, Y., Zhang, X., Duan, Y., & Li, N. L. (2017). Social media based transportation research: The state of the work and the networking. IEEE/CAA Journal of Automatica Sinica, 4(1), 19-26. https://doi.org/10.1109/JAS.2017.7510316
  22. Dayu, S., Huaiyu, X., Ruidan, S., & Zhiqiang, Y. (2010, November). A GEO-related IOT applications platform based on Google Map. In 2010 IEEE 7th International Conference on E-Business Engineering (pp. 380-384). IEEE Computer Society.
  23. SUCIU, G., BALANEAN, C., PASAT, A., ISTRATE, C., Hussain, I. J. A. Z., & MATEI, R. (2020, June). A new concept of smart shopping platform based on IoT solutions. In 2020 12th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) (pp. 1-4). IEEE.
  24. Ghareeb, M., Bazzi, A., Abdul-Nabi, S., & Ibrahim, Z. A. A. (2018). Towards smarter city: clever school transportation system. Analog Integrated Circuits and Signal Processing, 96(2), 261-268. https://doi.org/10.1007/s10470-018-1146-0
  25. Jan, B., Farman, H., Khan, M., Talha, M., & Din, I. U. (2019). Designing a smart transportation system: an internet of things and big data approach. IEEE Wireless Communications, 26(4), 73-79. https://doi.org/10.1109/MWC.2019.1800512
  26. Liu, M. (2021, July). Urban smart transportation based on big data. In Journal of Physics: Conference Series (Vol. 1972, No. 1, p. 012092). IOP Publishing.
  27. 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.
  28. Hahn, D., Munir, A., & Behzadan, V. (2019). 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
  29. Wernke, M., Skvortsov, P., Durr, F., & Rothermel, K. (2014). A classification of location privacy attacks and approaches. Personal and ubiquitous computing, 18(1), 163-175. https://doi.org/10.1007/s00779-012-0633-z
  30. Eian, I. C., Lim, K. Y., Yeap, M. X. L., Yeo, H. Q., & Fatima, Z. (2020). Wireless Networks: Active and Passive Attack Vulnerabilities and Privacy Challenges.
  31. Gerber, N., Reinheimer, B., & Volkamer, M. (2019). Investigating People's Privacy Risk Perception. Proc. Priv. Enhancing Technol., 2019(3), 267-288. https://doi.org/10.2478/popets-2019-0047
  32. Seliem, M., Elgazzar, K., & Khalil, K. (2018). Towards privacy preserving iot environments: a survey. Wireless Communications and Mobile Computing, 2018.
  33. Tawalbeh, L. A., Muheidat, F., Tawalbeh, M., & Quwaider, M. (2020). IoT Privacy and security: Challenges and solutions. Applied Sciences, 10(12), 4102.
  34. Barrett, L. (2018). Confiding in Con Men: US privacy law, the GDPR, and information fiduciaries. Seattle UL Rev., 42, 1057.
  35. Zaeem, R. N., & Barber, K. S. (2020). The effect of the GDPR on privacy policies: Recent progress and future promise. ACM Transactions on Management Information Systems (TMIS), 12(1), 1-20.
  36. Bruschi, D. M. (2019). Information privacy: Not just gdpr. In Computer Ethics Philosiphical Enquiry (pp. 1-10). ODU Digital Commons.
  37. Sen, A., Ahmed, A., Eassa, F. A., Jambi, K., & Yamin, M. (2018). Preserving privacy in internet of things: a survey. International Journal of Information Technology, 10(2), 189-200. https://doi.org/10.1007/s41870-018-0113-4
  38. Atlam, H. F., & Wills, G. B. (2020). IoT security, privacy, safety and ethics. In Digital twin technologies and smart cities (pp. 123-149). Springer, Cham.
  39. Solove, D. J. (2005). A taxonomy of privacy. U. Pa. l. Rev., 154, 477.
  40. Denning, D. E., & Denning, P. J. (1979). Data security. ACM Computing Surveys (CSUR), 11(3), 227-249. https://doi.org/10.1145/356778.356782
  41. Abi Sen, A. A., & Basahel, A. M. (2019, March). A comparative study between security and privacy. In 2019 6th International Conference on Computing for Sustainable Global Development (INDIACom) (pp. 1282-1286). IEEE.
  42. Malik, M. B., Ghazi, M. A., & Ali, R. (2012, November). Privacy preserving data mining techniques: current scenario and future prospects. In 2012 third international conference on computer and communication technology (pp. 26-32). IEEE.
  43. Daubert, J., Wiesmaier, A., & Kikiras, P. (2015, June). A view on privacy & trust in IoT. In 2015 IEEE International Conference on Communication Workshop (ICCW) (pp. 2665-2670). IEEE.
  44. Liu, X., Zhao, H., Pan, M., Yue, H., Li, X., & Fang, Y. (2012, March). Traffic-aware multiple mix zone placement for protecting location privacy. In 2012 Proceedings IEEE INFOCOM (pp. 972-980). IEEE.
  45. Gupta, A., & Bhartiya, R. (2017, August). A result evaluation on anonymiser and active object based TTP location privacy framework. In 2017 International Conference on Information, Communication, Instrumentation and Control (ICICIC) (pp. 1-6). IEEE.
  46. Ardagna, C. A., Cremonini, M., di Vimercati, S. D. C., & Samarati, P. (2009). An obfuscation-based approach for protecting location privacy. IEEE Transactions on Dependable and Secure Computing, 8(1), 13-27. https://doi.org/10.1109/TDSC.2009.25
  47. Fawaz, A., Hojaij, A., Kobeissi, H., & Artail, H. (2011, August). Using cooperation among peers and interest mixing to protect privacy in targeted mobile advertisement. In 2011 11th International Conference on ITS Telecommunications (pp. 474-479). IEEE.
  48. Zhangwei, H., & Mingjun, X. (2010, April). A distributed spatial cloaking protocol for location privacy. In 2010 Second International Conference on Networks Security, Wireless Communications and Trusted Computing (Vol. 2, pp. 468-471). IEEE.
  49. Grissa, M., Yavuz, A. A., & Hamdaoui, B. (2019). Location privacy in cognitive radios with multi-server private information retrieval. IEEE Transactions on Cognitive Communications and Networking, 5(4), 949-962. https://doi.org/10.1109/TCCN.2019.2922300
  50. Siddiqie, S., Mondal, A., & Reddy, P. K. (2021, April). An Improved Dummy Generation Approach for Enhancing User Location Privacy. In International Conference on Database Systems for Advanced Applications (pp. 487-495). Springer, Cham.
  51. Abi Sen, A. A., Alnsour, A., Aljwair, S. A., Aljwair, S. S., Alnafisah, H. I., & Altamimi, B. A. (2021, March). Fog mix-zone approach for preserving privacy in iot. In 2021 8th International Conference on Computing for Sustainable Global Development (INDIACom) (pp. 405-408). IEEE.
  52. Didouh, A., El Hillali, Y., Rivenq, A., & Labiod, H. (2022). Novel Centralized Pseudonym Changing Scheme for Location Privacy in V2X Communication. Energies, 15(3), 692.
  53. Wagner, I., & Eckhoff, D. (2018). Technical privacy metrics: a systematic survey. ACM Computing Surveys (CSUR), 51(3), 1-38.
  54. Yamin, M., Alsaawy, Y., B Alkhodre, A., Sen, A., & Ahmed, A. (2019). An innovative method for preserving privacy in Internet of Things. Sensors, 19(15), 3355.
  55. Jiang, H., Li, J., Zhao, P., Zeng, F., Xiao, Z., & Iyengar, A. (2021). Location privacy-preserving mechanisms in location-based services: A comprehensive survey. ACM Computing Surveys (CSUR), 54(1), 1-36.
  56. Wang, J., Cai, Z., & Yu, J. (2019). Achieving personalized $ k $-anonymity-based content privacy for autonomous vehicles in CPS. IEEE Transactions on Industrial Informatics, 16(6), 4242-4251.Author, Title of the Book, Publishing House, 200X.  https://doi.org/10.1109/TII.2019.2950057