Data Security and Privacy Challenges in IoT-Enabled Smart Cities: A Comprehensive Survey
الموضوعات : نشریه بینالمللی هوش تصمیمصفورا اخلاقی 1 , Mohammad bagher Menhaj 2 , behrooz masoumi 3
1 -
2 - Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran
3 - faculty member of computer Engineering, Department of Electrical Engineering and Computer, Qazvin, Iran
الکلمات المفتاحية: Smart city, Internet of Things, Security, Privacy, Fog computing, Artificial intelligence,
ملخص المقالة :
The rapid expansion of IoT technology has given rise to smart cities, but their complex architecture poses security challenges at various levels. Security and privacy are paramount requirements in the development of smart cities, particularly with the proliferation of IoT devices and data-driven systems.Since the privacy and security of smart city data in the IoT is a very up-to-date topic, researchers have addressed this issue, and several review articles have been conducted in this field. Addressing the intricate challenges posed by these advancements is crucial to safeguarding sensitive information against evolving attack vectors. This paper introduces a systematic literature review method to investigate privacy and security in IoT-based smart cities, analyzing research from 2016-2024. We present a taxonomy based on smart city architecture, categorizing privacy and security into low, middle, and high levels. This study reviews and categorizes articles, discusses their findings, methods, benefits, and drawbacks, and highlights future research areas in smart city security and privacy.
[1] Ogonji, M. M., Okeyo, G., &Wafula, J. M. (2020). A survey on privacy and security of Internet of Things. Computer Science Review, 38, 100312.
[2] Cunha, M., Mendes, R., & Vilela, J. P. (2021). A survey of privacy-preserving mechanisms for heterogeneous data types. Computer Science Review, 41, 100403.
[3] Sarwar, K., Yongchareon, S., & Yu, J. (2018, November). A brief survey on IoT privacy: taxonomy, issues and future trends. In International Conference on Service-Oriented Computing (pp. 208-219). Springer, Cham.
[4] Lau, B. P. L., Marakkalage, S. H., Zhou, Y., Hassan, N. U., Yuen, C., Zhang, M., & Tan, U. X. (2019). A survey of data fusion in smart city applications. Information Fusion, 52, 357-374.
[5] Al‐Turjman, F., Zahmatkesh, H., &Shahroze, R. (2019). An overview of security and privacy in smart cities' IoT communications. Transactions on Emerging Telecommunications Technologies, e3677.
[6] Akil, M., Islami, L., Fischer-Hübner, S., Martucci, L. A., &Zuccato, A. (2020). Privacy-preserving identifiers for IoT: a systematic literature review. IEEE Access, 8, 168470-168485.
[7] Huo, Y., Meng, C., Li, R., & Jing, T. (2020). An overview of privacy preserving schemes for industrial Internet of things. China Communications, 17(10), 1-18.
[8] Safa, N. S., Mitchell, F., Maple, C., Azad, M. A., &Dabbagh, M. (2020). Privacy Enhancing Technologies (PETs) for connected vehicles in smart cities. Transactions on Emerging Telecommunications Technologies, e4173.
[9] Curzon, J., Almehmadi, A., & El-Khatib, K. (2019). A survey of privacy enhancing technologies for smart cities. Pervasive and Mobile Computing, 55, 76-95.
[10] Karale, A. (2021). The Challenges of IoT Addressing Security, Ethics, Privacy, and Laws. Internet of Things, 15, 100420.
[11] Lau, B. P. L., Marakkalage, S. H., Zhou, Y., Hassan, N. U., Yuen, C., Zhang, M., & Tan, U. X. (2019). A survey of data fusion in smart city applications. Information Fusion, 52, 357-374.
[12] Vergara-Laurens, I. J., Jaimes, L. G., & Labrador, M. A. (2016). Privacy-preserving mechanisms for crowdsensing: Survey and research challenges. IEEE Internet of Things Journal, 4(4), 855-869.
[13] Jayaraman, P. P., Yang, X., Yavari, A., Georgakopoulos, D., & Yi, X. (2017). Privacy preserving Internet of Things: From privacy techniques to a blueprint architecture and efficient implementation. Future Generation Computer Systems, 76, 540-549.
[14] Seliem, M., Elgazzar, K., & Khalil, K. (2018). Towards privacy preserving iot environments: a survey. Wireless Communications and Mobile Computing, 2018(1), 1032761.
[15] Xu, G., Li, H., Tan, C., Liu, D., Dai, Y., & Yang, K. (2017). Achieving efficient and privacy-preserving truth discovery in crowd sensing systems. Computers & Security, 69, 114-126.
[16] Gheisari, M., Wang, G., & Chen, S. (2020). An edge computing-enhanced internet of things framework for privacy-preserving in smart city. Computers & Electrical Engineering, 81, 106504.
[17] Liu, H., Zhang, P., Pu, G., Yang, T., Maharjan, S., & Zhang, Y. (2020). Blockchain empowered cooperative authentication with data traceability in vehicular edge computing. IEEE Transactions on Vehicular Technology, 69(4), 4221-4232.
[18] Ma, Z., Ma, J., Miao, Y., Liu, X., Choo, K. K. R., Yang, R., & Wang, X. (2020). Lightweight privacy-preserving medical diagnosis in edge computing. IEEE Transactions on Services Computing.
[19] Rani, B. J. B., & ME, L. S. (2020). Outlier Detection in IoT Using Generative Adversarial Network..
[20] Derhab, A., Aldweesh, A., Emam, A. Z., & Khan, F. A. (2020). Intrusion detection system for Internet of things based on temporal convolution neural network and efficient feature engineering. Wireless Communications and Mobile Computing, 2020.
[21] Lin, Z., Shi, Y., & Xue, Z. (2018). Idsgan: Generative adversarial networks for attack generation against intrusion detection. arXiv preprint arXiv:1809.02077..
[22] Cheng, Y., Xu, Y., Zhong, H., & Liu, Y. (2020). Leveraging semisupervised hierarchical stacking temporal convolutional network for anomaly detection in IoT communication. IEEE Internet of Thingsss Journal, 8(1), 144-155.
[23] Reddy, D. K., Behera, H. S., Nayak, J., Vijayakumar, P., Naik, B., & Singh, P. K. (2021). Deep neural network-based anomaly detection in Internet of Things network traffic tracking for the applications of future smart cities. Transactions on Emerging Telecommunications Technologies, 32(7), e4121.
[24] Yin, C., Zhang, S., Wang, J., & Xiong, N. N. (2020). Anomaly detection based on convolutional recurrent autoencoder for IoT time series. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(1), 112-122.
[25] Ni, J., Zhang, K., Yu, Y., Lin, X., & Shen, X. S. (2018). Providing task allocation and secure deduplication for mobile crowdsensing via fog computing. IEEE Transactions on Dependable and Secure Computing, 17(3), 581-594.
[26] Ni, J., Lin, X., & Shen, X. S. (2018). Efficient and secure service-oriented authentication supporting network slicing for 5G-enabled IoT. IEEE Journal on Selected Areas in Communications, 36(3), 644-657.
[27] Wang, Q., & Mu, H. (2021). Privacy-Preserving and Lightweight Selective Aggregation with Fault-Tolerance for Edge Computing-Enhanced IoT. Sensors, 21(16), 5369.
[28] Zhang, J., Zhao, Y., Wu, J., & Chen, B. (2020). LVPDA: A lightweight and verifiable privacy-preserving data aggregation scheme for edge-enabled IoT. IEEE Internet of Things Journal, 7(5), 4016-4027.
[29] Daoudagh, S., Marchetti, E., Savarino, V., Bernabe, J. B., García-Rodríguez, J., Moreno, R. T., ... &Skarmeta, A. F. (2021). Data Protection by Design in the Context of Smart Cities: A Consent and Access Control Proposal. Sensors, 21(21), 7154.
[30] Tao, M., Zuo, J., Liu, Z., Castiglione, A., & Palmieri, F. (2018). Multilayer cloud architectural model and ontology-based security service framework for IoT-based smart homes. Future Generation Computer Systems, 78, 1040-1051.
[31] Deebak, B. D., & Al-Turjman, F. (2020). Smart mutual authentication protocol for cloud based medical healthcare systems using Internet of medical things. IEEE Journal on Selected Areas in Communications, 39(2), 346-360.
[32] Xiong, H., Zhang, H., & Sun, J. (2018). Attribute-based privacy-preserving data sharing for dynamic groups in cloud computing. IEEE Systems Journal, 13(3), 2739-2750.
[33] Yang, Y., Zheng, X., Guo, W., Liu, X., & Chang, V. (2019). Privacy-preserving smart IoT-based healthcare big data storage and self-adaptive access control system. Information Sciences, 479, 567-592.
[34] Qolomany, B., Mohammed, I., Al-Fuqaha, A., Guizani, M., & Qadir, J. (2020). Trust-based cloud machine learning model selection for industrial IoT and smart city services. IEEE Internet of Things Journal, 8(4), 2943-2958.
[35] Jyothi, V., Tammineni, S., Thiyagu, T. M., Sowndharya, R., & Arvinth, N. (2024). A Data Management System for Smart Cities Leveraging Artificial Intelligence Modeling Techniques to Enhance Privacy and Security. Journal of Internet Services and Information Security, 14(1), 37-51.
[36] Nyangaresi, V. O., Abduljabbar, Z. A., Mutlaq, K. A. A., Bulbul, S. S., Ma, J., Aldarwish, A. J., ... & Neamah, H. A. (2024). Smart city energy efficient data privacy preservation protocol based on biometrics and fuzzy commitment scheme. Scientific Reports, 14(1), 16223.
[37] Selvarajan, S., Manoharan, H., Goel, S., Akili, C. P., Murugesan, S., & Joshi, V. (2024). SCMC: Smart city measurement and control process for data security with data mining algorithms. Measurement: Sensors, 31, 100980.
[38] Desai, B., Patil, K., Mehta, I., & Patil, A. (2024). A Secure Communication Framework for Smart City Infrastructure Leveraging Encryption, Intrusion Detection, and Blockchain Technology. Advances in Computer Sciences, 7(1).
