Blockchain-based Security in Cloud and Edge Computing for AI Applications
DOI:
https://doi.org/10.63282/3050-922X.ICRCEDA25-120Keywords:
Blockchain Technology, Cloud Computing, Edge Computing, AI Security, Distributed Ledger, Data Integrity, Authentication, Access Control, Decentralization, Blockchain Integration, Cloud-Edge SecurityAbstract
The integration of Artificial Intelligence (AI) with cloud and edge computing has revolutionized various industries, offering increased computational power and reduced latency for data processing. However, as AI systems rely heavily on distributed resources, security becomes a critical concern, particularly regarding data integrity, authentication, and access control. Blockchain technology, with its decentralized and immutable nature, offers promising solutions to these challenges. This paper explores the potential of blockchain-based security frameworks in cloud and edge computing environments to enhance the security of AI applications. We examine the benefits and challenges of incorporating blockchain into these systems, the impact on AI-driven security models, and the feasibility of implementing such solutions in real-world scenarios. The paper concludes with future directions for research and potential applications of blockchain in securing AI in cloud and edge environments
References
[1] Nakamoto, S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System. https://bitcoin.org/bitcoin.pdf
[2] Zohar, A., & Gueta, T. (2019). Blockchain: The New Age of Security for Cloud Computing. International Journal of Computer Science and Security, 13(2), 45-58.
[3] Xu, K., & Wang, X. (2020). Blockchain and Edge Computing: An Overview of Applications and Challenges. IEEE Transactions on Industrial Informatics, 16(8), 5145-5154.
[4] Sharma, S., & Gupta, A. (2021). Blockchain Integration in Edge Computing for Secure AI Applications. Journal of Cloud Computing: Advances, Systems, and Applications, 12(1), 22-35.
[5] Smith, L., & Hong, C. (2020). Security Issues and Solutions in Cloud Computing for AI Applications. Journal of Cloud Security, 8(4), 112-120.
[6] Lee, J., & Park, H. (2021). Blockchain and Artificial Intelligence for Cybersecurity in Cloud Environments. Journal of Artificial Intelligence Research, 48(6), 1032-1045.
[7] Tsai, W., & Lin, J. (2020). Decentralized Access Control Using Blockchain Technology in Cloud Services for AI Security. IEEE Access, 8, 194211-194222.
[8] Jiang, X., & Liu, T. (2021). Securing Autonomous Vehicles Using Blockchain and Edge Computing for AI-Based Systems. Journal of Intelligent Transportation Systems, 25(3), 215-226.
[9] Tan, S., & Chen, Z. (2022). Blockchain-Based Security for Healthcare AI in Cloud and Edge Environments. International Journal of Healthcare Informatics, 34(4), 85-98.
[10] Zhao, Y., & Zhang, P. (2019). Blockchain in Edge Computing for Securing Industrial IoT Applications: A Review. International Journal of Industrial Electronics, 41(7), 1248-1259.
[11] Wang, Y., & Yang, H. (2020). AI-Powered Blockchain Security Models for Cloud Computing. Journal of Computing and Security, 39(1), 37-45.
[12] Patel, V., & Mehta, S. (2021). Quantum-Resistant Blockchain Solutions for AI Security in Cloud and Edge Systems. Future Generation Computer Systems, 118, 347-360.
[13] Liu, F., & Li, M. (2021). Improving AI Security with Blockchain in Edge Computing Networks. Journal of Edge Computing, 5(2), 101-115.
[14] Choi, S., & Kim, Y. (2020). Blockchain Applications in AI and Cloud Computing: Challenges and Future Directions. Journal of Computational Intelligence, 31(6), 1342-1354.
[15] Hossain, M., & Hasan, M. (2021). Leveraging Blockchain for Secure AI Models in Distributed Edge Computing Networks. IEEE Transactions on Network and Service Management, 18(5), 122-133.
[16] Animesh Kumar, “AI-Driven Innovations in Modern Cloud Computing”, Computer Science and Engineering, 14(6), 129-134, 2024.
[17] Kirti Vasdev. (2019). “GIS in Disaster Management: Real-Time Mapping and Risk Assessment”. International Journal on Science and Technology, 10(1), 1–8. https://doi.org/10.5281/zenodo.14288561
[18] Vegineni, Gopi Chand, and Bhagath Chandra Chowdari Marella. "Integrating AI-Powered Dashboards in State Government Programs for Real-Time Decision Support." AI-Enabled Sustainable Innovations in Education and Business, edited by Ali Sorayyaei Azar, et al., IGI Global, 2025, pp. 251-276. https://doi.org/10.4018/979-8-3373-3952-8.ch011
[19] Kodi, D. (2024). “Performance and Cost Efficiency of Snowflake on AWS Cloud for Big Data Workloads”. International Journal of Innovative Research in Computer and Communication Engineering, 12(6), 8407–8417. https://doi.org/10.15680/IJIRCCE.2023.1206002
[20] S. Gupta, S. Barigidad, S. Hussain, S. Dubey and S. Kanaujia, "Hybrid Machine Learning for Feature-Based Spam Detection," 2025 2nd International Conference on Computational Intelligence, Communication Technology and Networking (CICTN), Ghaziabad, India, 2025, pp. 801-806, doi: 10.1109/CICTN64563.2025.10932459.
[21] Puneet Aggarwal,Amit Aggarwal. "AI-Driven Supply Chain Optimization in ERP Systems Enhancing Demand Forecasting and Inventory Management", International Journal of Management, IT & Engineering, 13 (8), 107-124, 2023.
[22] Sahil Bucha, “Integrating Cloud-Based E-Commerce Logistics Platforms While Ensuring Data Privacy: A Technical Review,” Journal Of Critical Reviews, Vol 09, Issue 05 2022, Pages1256-1263.
[23] L. N. R. Mudunuri, V. M. Aragani, and P. K. Maroju, "Enhancing Cybersecurity in Banking: Best Practices and Solutions for Securing the Digital Supply Chain," Journal of Computational Analysis and Applications, vol. 33, no. 8, pp. 929-936, Sep. 2024.
[24] S. Panyaram, "Automation and Robotics: Key Trends in Smart Warehouse Ecosystems," International Numeric Journal of Machine Learning and Robots, vol. 8, no. 8, pp. 1-13, 2024.
[25] Gopichand Vemulapalli, Padmaja Pulivarthy, “Integrating Green Infrastructure With AI-Driven Dynamic Workload Optimization: Focus on Network and Chip Design,” in Integrating Blue-Green Infrastructure Into Urban Development, IGI Global, USA, pp. 397-422, 2025.
[26] Lakshmi Narasimha Raju Mudunuri, Praveen Kumar Maroju, Venu Madhav Aragani, (2025/1/9), Leveraging NLP-Driven Sentiment Analysis for Enhancing Decision-Making in Supply Chain Management. 2025 Fifth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), 1-6 IEEE.
[27] L. N. Raju Mudunuri, P. K. Maroju and V. M. Aragani, "Leveraging NLP-Driven Sentiment Analysis for Enhancing Decision-Making in Supply Chain Management," 2025 Fifth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), Bhilai, India, 2025, pp. 1-6, doi: 10.1109/ICAECT63952.2025.10958844.
[28] Mohanarajesh, Kommineni (2024). Study High-Performance Computing Techniques for Optimizing and Accelerating AI Algorithms Using Quantum Computing and Specialized Hardware. International Journal of Innovations in Applied Sciences and Engineering 9 (`1):48-59.
[29] Puvvada, R. K. "The Impact of SAP S/4HANA Finance on Modern Business Processes: A Comprehensive Analysis." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11.2 (2025): 817-825.
[30] Advanced Technique for Analysis of the Impact on Performance Impact on Low-Carbon Energy Systems by Plant Flexibility, Sree Lakshmi Vineetha Bitragunta1 , Lakshmi Sneha Bhuma2 , Gunnam Kushwanth3, International Journal for Multidisciplinary Research (IJFMR), Volume 2, Issue 6, November-December 2020,PP-1-9.
[31] Kirti Vasdev (2024).” Spatial Data Clustering and Pattern Recognition Using Machine Learning”. International Journal for Multidisciplinary Research (IJFMR).6(1). PP. 1-6. DOI: https://www.ijfmr.com/papers/2024/1/23474
[32] Sahil Bucha, “Design And Implementation of An AI-Powered Shipping Tracking System For E-Commerce Platforms”, Journal of Critical Reviews, Vol 10, Issue 07, 2023, Pages. 588-596.
[33] Jagadeesan Pugazhenthi, V., Singh, J., & Pandy, G. (2025). Revolutionizing IVR Systems with Generative AI for Smarter Customer Interactions. International Journal of Innovative Research in Computer and Communication Engineering, 13(1).
[34] Kiran Nittur, Srinivas Chippagiri, Mikhail Zhidko, “Evolving Web Application Development Frameworks: A Survey of Ruby on Rails, Python, and Cloud-Based Architectures”, International Journal of New Media Studies (IJNMS), 7 (1), 28-34, 2020.
[35] Islam Uddin, Salman A. AlQahtani, Sumaiya Noor, Salman Khan. “Deep-m6Am: a deep learning model for identifying N6, 2′-O-Dimethyladenosine (m6Am) sites using hybrid features[J]”. AIMS Bioengineering, 2025, 12(1): 145-161. doi: 10.3934/bioeng.2025006.
[36] Arpit Garg, “CNN-Based Image Validation for ESG Reporting: An Explainable AI and Blockchain Approach”, Int. J. Comput. Sci. Inf. Technol. Res., vol. 5, no. 4, pp. 64–85, Dec. 2024, doi: 10.63530/IJCSITR_2024_05_04_007
[37] Venkata Krishna Reddy Kovvuri. (2024). Next-Generation Cloud Technologies: Emerging Trends In Automation And Data Engineering. International Journal Of Research In Computer Applications And Information Technology (Ijrcait),7(2),1499-1507.
[38] Vootkuri, C. Measuring Cloud Security Maturity: A Hybrid Approach Combining AI and Automation.
[39] Batchu, R.K., Settibathini, V.S.K. (2025). Sustainable Finance Beyond Banking Shaping the Future of Financial Technology. In: Whig, P., Silva, N., Elngar, A.A., Aneja, N., Sharma, P. (eds) Sustainable Development through Machine Learning, AI and IoT. ICSD 2024. Communications in Computer and Information Science, vol 2196. Springer, Cham. https://doi.org/10.1007/978-3-031-71729-1_12
