Leveraging AI and Blockchain for Privacy Preservation and Security in Fog Computing
DOI:
https://doi.org/10.4108/eetiot.5555Keywords:
Artificial Intelligence, Fog computing, Privacy Preservation Model, Cloud ComputingAbstract
INTRODUCTION: Cloud computing's offshoot, fog computing, moves crucial data storage, processing, and networking capabilities closer to the people who need them. There are certain advantages, such improved efficiency and lower latency, but there are also some major privacy and security concerns. For these reasons, this article presents a new paradigm for fog computing that makes use of blockchain and Artificial Intelligence (AI).
OBJECTIVES: The main goal of this research is to create and assess a thorough framework for fog computing that incorporates AI and blockchain technology. With an emphasis on protecting the privacy and integrity of data transactions and streamlining the management of massive amounts of data, this project seeks to improve the security and privacy of Industrial Internet of Things (IIoT) systems that are cloud-based.
METHODS: Social network analysis methods are utilised in this study. The efficiency and accuracy of data processing in fog computing are guaranteed by the application of artificial intelligence, most especially Support Vector Machine (SVM), due to its resilience in classification and regression tasks. The network's security and reliability are enhanced by incorporating blockchain technology, which creates a decentralised system that is tamper resistant. To make users' data more private, zero-knowledge proof techniques are used to confirm ownership of data without actually disclosing it.
RESULTS: When applied to fog computing data, the suggested approach achieves a remarkable classification accuracy of 99.8 percent. While the consensus decision-making process of the blockchain guarantees trustworthy and secure operations, the support vector machine (SVM) efficiently handles massive data analyses. Even in delicate situations, the zero-knowledge proof techniques manage to keep data private. When these technologies are integrated into the fog computing ecosystem, the chances of data breaches and illegal access are greatly reduced.
CONCLUSION: Fog computing, which combines AI with blockchain, offers a powerful answer to the privacy and security issues with cloud centric IIoT systems. Combining SVM with AI makes data processing more efficient, while blockchain's decentralised and immutable properties make it a strong security measure. Additional security for user privacy is provided via zero-knowledge proofs. Improving the privacy and security of fog computing networks has never been easier than with this novel method.
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Ferrag, M.A., Derhab, A., Maglaras, L., Mukherjee, M., Janicke, H.: Privacy-preserving Schemes for Fog-based IoT Applications: Threat models, Solutions, and Challenges. 2018 International Conference on Smart Communications in Network Technologies (SaCoNeT) pp. 37–42 (2018) DOI: https://doi.org/10.1109/SaCoNeT.2018.8585538
Gowda, N.C., Manvi, S.S., M, B.: Blockchain-based Access Control Model with Privacy preservation in a Fog Computing Environment. 2022 IEEE International Conference on Elec- tronics, Computing and Communication Technologies (CONECCT) pp. 1–6 (2022) DOI: https://doi.org/10.1109/CONECCT55679.2022.9865845
Chen, S., Zhu, X., Zhang, H., Zhao, C., Yang, G., Wang, K.: Efficient Privacy Preserving Data Collection and Computation Offloading for Fog-Assisted IoT. IEEE Transactions on Sustainable Computing 5, 526–540 (2020) DOI: https://doi.org/10.1109/TSUSC.2020.2968589
Lai, C., Li, Q., Zhou, H., Zheng, D.: SRSP: A Secure and Reliable Smart Parking Scheme With Dual Privacy Preservation. IEEE Internet of Things Journal 8(13), 10619–10630 (2021) DOI: https://doi.org/10.1109/JIOT.2020.3048177
Zhonghua, C., Goyal, S.B., Rajawat, A.S.: Smart contracts attribute-based access control model for security & privacy of IoT system using blockchain and edge computing. J Super- comput (2023) DOI: https://doi.org/10.1007/s11227-023-05517-4
Huynh-The, T., Gadekallu, T.R., Wang, W., Yenduri, G., Ranaweera, P., Pham, Q.V., Costa, D.B.D., Liyanage, M.: Blockchain for the metaverse: A Review. Future Generation Com- puter Systems 143, 401–419 (2023)
Huynh-The, T., Gadekallu, T.R., Wang, W., Yenduri, G., Ranaweera, P., Pham, Q.V., Costa, D., Liyanage, M.: Blockchain for the metaverse: A Review. Future Generation Com- puter Systems 143, 401–419 (2023) DOI: https://doi.org/10.1016/j.future.2023.02.008
Rajawat, A.S.: Blockchain-based Security Framework for Metaverse: A Decentralized Ap- proach. In: 2023 15th International Conference on Electronics, Computers and Artificial Intelligence (ECAI). pp. 1–06 (2023) DOI: https://doi.org/10.1109/ECAI58194.2023.10193962
Pundir, S., Wazid, M., Singh, D.P., Das, A.K., Rodrigues, J.J.P.C., Park, Y.: Intrusion De- tection Protocols in Wireless Sensor Networks Integrated to Internet of Things Deployment: Survey and Future Challenges. IEEE Access 8, 3343–3363 (2020) DOI: https://doi.org/10.1109/ACCESS.2019.2962829
Luong, T.D.: FedChain: A Collaborative Framework for Building Artificial Intelligence Models using Blockchain and Federated Learning. 2021 8th NAFOSTED Conference on Information and Computer Science (NICS) pp. 149–154 (2021) DOI: https://doi.org/10.1109/NICS54270.2021.9701450
Rajawat, A.S., Goyal, S.B., Bedi, P., Verma, C., Ionete, E.I., Raboaca, M.: https://doi.org/10. 3390/math11030679
Zerka, F.: Blockchain for Privacy Preserving and Trustworthy Distributed Machine Learning in Multicentric Medical Imaging (C-DistriM). IEEE Access 8, 183939–183951 (2020) DOI: https://doi.org/10.1109/ACCESS.2020.3029445
Dave, M., Rastogi, V., Miglani, M.: Smart Fog-Based Video Surveillance with Privacy Preservation based on Blockchain. Wireless Pers Commun 124, 1677–1694 (2022) DOI: https://doi.org/10.1007/s11277-021-09426-8
Alzoubi, Y.I., Gill, A., Mishra, A.: A systematic review of the purposes of Blockchain and fog computing integration: classification and open issues. J Cloud Comp 11, 80–80 (2022) DOI: https://doi.org/10.1186/s13677-022-00353-y
Shah, K., Chadotra, S., Tanwar, S.: Blockchain for IoV in 6G environment: review solutions and challenges. Cluster Comput 25 (1927) DOI: https://doi.org/10.1007/s10586-021-03492-0
Li, W., Wu, J., Cao, J.: Blockchain-based trust management in cloud computing systems: a taxonomy, review and future directions. J Cloud Comp 10, 35–35 (2021) DOI: https://doi.org/10.1186/s13677-021-00247-5
Krishnamoorthy, S., Dua, A., Gupta, S.: Role of emerging technologies in future IoT-driven Healthcare 4.0 technologies: a survey, current challenges and future directions. J Ambient Intell Human Comput (2021) DOI: https://doi.org/10.1007/s12652-021-03302-w
18. Amiri, Z., Heidari, A., Navimipour, N.J.: (2022), https://doi.org/10.1007/s10586-022- 03738-5
Singh, A., Satapathy, S.C., Roy, A.: AI-Based Mobile Edge Computing for IoT: Applications, Challenges, and Future Scope. Arab J Sci Eng 47, 9801–9831 (2022) DOI: https://doi.org/10.1007/s13369-021-06348-2
Bagga, P., Das, A.K., Chamola, V.: Blockchain-envisioned access control for internet of things applications: a comprehensive survey and future directions. Telecommun Syst 81, 125–173 (2022) DOI: https://doi.org/10.1007/s11235-022-00938-7
Alfa, A.A., Alhassan, J.K., Olaniyi, O.M.: Blockchain technology in IoT systems: current trends, methodology, problems, applications, and future directions. J Reliable Intell Environ 7, 115–143 (2021) DOI: https://doi.org/10.1007/s40860-020-00116-z
Bhushan, B., Sahoo, C., Sinha, P.: Unification of Blockchain and Internet of Things (BIoT): requirements, working model, challenges and future directions. Wireless Netw 27, 55–90 (2021) DOI: https://doi.org/10.1007/s11276-020-02445-6
Alagheband, M.R., Mashatan, A.: Advanced encryption schemes in multi-tier heterogeneous internet of things: taxonomy, capabilities, and objectives. J Supercomput 78, 18777–18824 (2022) DOI: https://doi.org/10.1007/s11227-022-04586-1
Rejeb, A., Rejeb, K., Simske, S.J.: Blockchain technology in the smart city: a bibliometric review. Qual Quant 56, 2875–2906 (2022) DOI: https://doi.org/10.1007/s11135-021-01251-2
Wang, C., Cheng, X., Li, J.: A survey: applications of blockchain in the Internet of Vehicles. J Wireless Com Network 2021, 77–77 (2021) DOI: https://doi.org/10.1186/s13638-021-01958-8
Shafay, M., Ahmad, R.W., Salah, K.: Blockchain for deep learning: review and open chal- lenges. Cluster Comput (2022) DOI: https://doi.org/10.36227/techrxiv.16823140
Himeur, Y., Elnour, M., Fadli, F.: AI-big data analytics for building automation and manage- ment systems: a survey, actual challenges and future perspectives. Artif Intell Rev (2022) DOI: https://doi.org/10.1007/s10462-022-10286-2
Li, D., Han, D., Weng, T.H.: Blockchain for federated learning toward secure distributed machine learning systems: a systemic survey. Soft Comput 26, 4423–4440 (2022) DOI: https://doi.org/10.1007/s00500-021-06496-5
Elrahman, S.A., Alluhaidan, A.S.: Blockchain technology and IoT-edge framework for shar- ing healthcare services. Soft Comput 25, 13753–13777 (2021) DOI: https://doi.org/10.1007/s00500-021-06041-4
Jiang, M., Qin, X.: Distributed ledger technologies in vehicular mobile edge computing: a survey. Complex Intell. Syst 8, 4403–4419 (2022) DOI: https://doi.org/10.1007/s40747-021-00603-7
Su, W., Li, L., Liu, F.: AI on the edge: a comprehensive review. Artif Intell Rev 55, 6125– 6183 (2022) DOI: https://doi.org/10.1007/s10462-022-10141-4
Selvarajan, S., Srivastava, G., Khadidos, A.O.: An artificial intelligence lightweight blockchain security model for security and privacy in IIoT systems. J Cloud Comp 12, 38–38 (2023) DOI: https://doi.org/10.1186/s13677-023-00412-y
Zubaydi, H.D., Varga, P., Molnár, S.: Leveraging Blockchain Technology for Ensuring Se- curity and Privacy Aspects in Internet of Things: A Systematic Literature Review. Sensors 23, 788–788 (2023) DOI: https://doi.org/10.3390/s23020788
Sameera, K.M., Vinod, P., Rehiman, K.A.R., Jifhna, P., Sebastian, S.: Blockchain Feder- ated Learning Framework for Privacy-Preservation. In: Rajagopal, S., Faruki, P., Popat, K. (eds.) Advancements in Smart Computing and Information Security. ASCIS 2022. vol. 1760. Springer (2022) DOI: https://doi.org/10.1007/978-3-031-23095-0_18
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