Blockchain-Driven Decentralized Identity Intelligence and Privacy-Preserving Security for Enterprise Cloud Data Protection
Main Article Content
Abstract
The rapid adoption of cloud computing has transformed enterprise data management while introducing significant challenges related to privacy, identity verification, access control, and regulatory compliance. Traditional identity management systems frequently depend on centralized authorities, creating potential single points of failure, unauthorized access risks, and concerns regarding the excessive collection of personal information. This study explores a blockchain-enabled privacy protection framework for enterprise cloud data using decentralized identity verification. The proposed approach integrates blockchain technology, decentralized identifiers (DIDs), verifiable credentials, cryptographic authentication, and privacy-preserving access control to strengthen data confidentiality and identity assurance. In this framework, users maintain greater control over their digital identities, while enterprises verify authentication claims without unnecessarily exposing sensitive personal information. Smart contracts facilitate transparent access authorization, credential validation, and auditable policy enforcement, whereas encrypted cloud storage protects enterprise information from unauthorized disclosure. The research methodology adopts a conceptual design and comparative evaluation approach to examine the framework's security, privacy, scalability, and operational efficiency. Relevant performance indicators include authentication latency, access verification overhead, storage efficiency, and resistance to identity-related attacks. The proposed framework aims to reduce dependence on centralized identity providers, improve accountability, and support privacy-aware enterprise cloud operations. The study provides a foundation for developing secure, scalable, and interoperable cloud identity management systems.
Article Details
Section
How to Cite
References
1. Wang, H., Wang, Q., Ding, Y., Tang, S., et al. (2024). Privacy-preserving federated learning based on partial low-quality data. Journal of Cloud Computing, 13, Article 62. https://doi.org/10.1186/s13677-024-00618-8
2. Neela, S. (2024). Secure and zero-trust middleware architectures for real-time enterprise data exchange. International Journal of AI, BigData, Computational and Management Studies, 5(4), 220–227.
3. Ahuja, D. (2023). Multi-Cloud Management Strategies: Tools, Challenges, and Implementation Methods. Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol, 9(10), 493–506.
4. Kalabhavi, V. (2024). Promotional spend analytics using AI for PFME allocation and coupon redemption optimization. International Journal of Research Publications in Engineering, Technology and Management, 7(2), 10417–10429.
5. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273–287.
6. Mysula, S. (2025). Engineering resilient enterprise integration platforms through automated monitoring and intelligent error management. International Journal of Computer Technology and Electronics Communication, 8(2), 10508–10513.
7. Katakam, R. (2023). Automated loan processing and payment reconciliation through integrated banking workflows. International Journal of Engineering & Extended Technologies Research, 5(5), 7313–7324.
8. Polamarasetty, V. K. (2024). Workday-based enterprise benefits management: Configuration, automation, and operational optimization. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(5), 11347–11352.
9. Beeram, S. (2025). Federated Learning with Azure IoT Edge and Azure Machine Learning for Privacy-Preserving Healthcare AI across US Hospital Networks. International Journal of Computer Science and Mobile Computing, 14(9), 119–123.
10. Chinthamani, R. (2023). Engineering resilient microservices for real-time retail order and inventory synchronization. International Journal of Engineering & Extended Technologies Research, 5(6), 7774–7779.
11. Madhan, E. S., Kannan, K. S., Rani, P. S., Rani, J. V., & Anguraj, D. K. (2021). A distributed submerged object detection and classification enhancement with deep learning. Distrib. Parallel Databases, 41, 1–17.
12. Tarigoppula, S. (2021). Policy-driven autonomous infrastructure provisioning: A framework for enterprise cloud governance using infrastructure as code. International Journal of Humanities and Information Technology, Special Issue 1, 131–141.
13. Gantikota, S. (2023). Reducing HL7 Processing Errors through Automated File Creation and Ingestion Pipelines: A Production Case Study in EHR Data Integration. International Journal of Emerging Trends in Computer Science and Information Technology, 4(4), 241–245.
14. Soundappan, S. J. (2021). DataOps: Orchestrating Reliable ML Data Pipelines. International Journal of Research and Applied Innovations, 4(4), 5533–5537.
15. Bandari, U., & Jinna, K. R. (2024). LLM-assisted anonymization of customer experience data: A comparative study of rule-based, NER, and generative approaches. International Journal of Research and Applied Innovations, 7(1), 10164–10174.
16. Venkatasalam, K., Rajendran, P., & Thangavel, M. (2019). Improving the accuracy of feature selection in big data mining using accelerated flower pollination (AFP) algorithm. Journal of medical systems, 43(4), 96.
17. Natta, P. K. (2025). AI-driven decision intelligence: Optimizing enterprise strategy with AI-augmented insights. Journal of Computer Science and Technology Studies, 7(2), 146–152.
18. Gopinathan, V. R., & Mali, R. K. (2022). Building cognitive technology frameworks through artificial intelligence SAP cloud automation and enterprise intelligence. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7152–7162.
19. Karrothu, A. (2025). Exploitability-aware vulnerability prioritization for automated security enforcement in cloud-native applications. International Journal of Research and Applied Innovations, 8(2), 11317–11331.
20. Garg, D. (2025). Warehouse management system with IoT: A comprehensive guide. International Journal of Computer Engineering & Technology, 16, 2320–2332.
21. Padmanabham, S. (2023). Secure API gateway architecture for open banking. International Journal of Computer Technology and Electronics Communication, 6(6), 8166–8174.
22. Ramasamy, M. (2022). Architecting scalable intent-based networking platforms for enterprise automation. International Journal of Engineering & Extended Technologies Research, 4(3), 4978–4989.
23. Basavala, S. R. (2025). Continuous application security posture management through AI-driven risk correlation and vulnerability prioritization. International Journal of Research Publications in Engineering, Technology and Management, 8(3), 12175–12187.
24. Kalakoti, M. K. R. (2023). Interference-aware resource scheduling for cloud microservices on Kubernetes. International Journal of Humanities and Information Technology, 5(1), 104–114.
25. Aragani, V. M., Maroju, P. K., & Mudunuri, L. N. R. (2024, December). Implementing Non-functional Production Regression Testing with Kubernetes. In International Conference on Information and Communication Technology for Competitive Strategies (pp. 231–244). Singapore: Springer Nature Singapore.
26. Selvarajan, K. (2022). Architecting scalable self-service data platforms for enterprise analytics. International Journal of Computer Technology and Electronics Communication, 5(2), 6684–6693.
27. Ravichandran, S., & Kandasamy, V. (2025). Optimized Attention Augmented Residual Convolutional Neural Network with Fa-Resnet for Fabric Defect Detection. Journal of Control Engineering and Applied Informatics, 27(4), 3–15.
28. Mounika, B., & Thota, S. (2021). A Multilayer Cloud–AI Framework for Intelligent Process Automation in Salesforce-Centric Digital Enterprises. International Journal of AI, BigData, Computational and Management Studies, 2(3), 99-109.
29. 31.Manoharan, D. (2024). Governance-Oriented Quality Engineering Framework for Healthcare EDI Modernization. International Journal of Multidisciplinary on Science and Management IJMSM, 1(2).
30. Chandran, A. S. (2023). A unified Spark ETL stack across Cloudera CDP, Azure HDInsight, and Databricks. International Journal of Future Innovative Science and Technology, 6(6), 11769–11779.
31. Mudunuri, P. R. (2024). Operational transparency as a compliance mechanism in federal DevOps ecosystems. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(3), 8131–8142.
32. Shaik, N. (2024). Right-sizing at scale: A data-driven approach to Kubernetes cost optimization. International Journal of Research Publications in Engineering, Technology and Management, 7(1), 10080–10090.
33. Yadavalli, V. R. (2025). Incident memory graphs for predictive AMS triage and self-improving SAP operations. International Journal of Research and Applied Innovations, 8(2), 11332–11346.
34. Kopparthi, G. S. (2023). Trustworthy artificial intelligence for high-stakes decision-making: Explainability, human oversight, and system reliability. International Journal of Engineering & Extended Technologies Research, 5(4), 6979–6992.
35. Jinna, K. R., & Thilagaraj, S. V. (2024). Tracing across the seam: OpenTelemetry-based observability for hybrid estates where message-oriented middleware and Kubernetes microservices coexist. International Journal of Humanities and Information Technology, 6(3), 167–177.
36. Gadige, C. D. (2024). Enterprise integration architecture for AI-powered CRM: Combining Salesforce, MuleSoft, data platforms, and autonomous agents. International Journal of Research and Applied Innovations, 7(3), 10850–10860.
37. Valavala, S. K. (2024). From application logs to audit evidence in financial microservices. International Journal of Research and Applied Innovations, 7(4), 11246–11256.
38. Gunasekaran, R. M. N. (2024). AI/ML and Generative AI Use Cases in Financial Services. The Eastasouth Journal of Information System and Computer Science, 2(02), 345–356.