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Secure Cloud Based Enterprise Analytics Framework Using Federated Learning for Privacy Preserving Data Collaboration

Dr.R.Sugumar

Professor, Department of Computer Science & Engineering, SIMATS Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, India

Abstract

In the era of data-driven decision-making, enterprises increasingly rely on large-scale analytics frameworks deployed in cloud environments. However, the centralization of sensitive business and customer data raises significant privacy, security, and regulatory concerns. This paper proposes a Secure Cloud-Based Enterprise Analytics Framework leveraging Federated Learning (FL) to enable privacy-preserving collaborative data analytics across distributed enterprise nodes. Instead of transferring raw data to a central server, the framework trains machine learning models locally at each participating organization and aggregates only model updates in a secure cloud environment. This approach significantly reduces the risk of data leakage while maintaining analytical performance. The proposed framework integrates cryptographic techniques, secure aggregation protocols, and differential privacy mechanisms to enhance confidentiality and resilience against adversarial attacks. It also supports heterogeneous enterprise environments where data distributions may vary across clients. The architecture is designed to ensure scalability, fault tolerance, and compliance with global data protection regulations such as GDPR. Experimental evaluations in similar federated environments demonstrate that the proposed approach achieves near-centralized model accuracy while significantly improving privacy preservation. The framework enables enterprises to collaboratively gain insights from distributed datasets without exposing sensitive information, making it suitable for industries such as healthcare, finance, and supply chain analytics.

Article Information

Journal

International Journal of Science, Research and Technology

Volume (Issue)

Vol. 8 No. 5 (2025): International Journal of Science, Research and Technology (IJSRAT)

DOI

https://doi.org/10.15662/IJSRAT.2025.0805005

Pages

14955-14961

Published

September 11, 2025
Copyright

Open Access

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Dr.R.Sugumar (%2025). Secure Cloud Based Enterprise Analytics Framework Using Federated Learning for Privacy Preserving Data Collaboration. International Journal of Science, Research and Technology , Vol. 8 No. 5 (2025): International Journal of Science, Research and Technology (IJSRAT) , pp. 14955-14961. https://doi.org/10.15662/IJSRAT.2025.0805005

References

1. Makkena, B. (2024). Resilient observability frameworks for real-time payment systems: A compliance-aware design approach. Journal of Information Systems Engineering and Management, 9(3).
2. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
3. Mohammed, S., & Polamarasetty, V. K. (2021). Enterprise multi-cloud transformation and managed services modernization. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 4(9), 2041–2056. https://doi.org/10.15680/IJMRSET.2021.0409023
4. Mathew, A., & Mai, C. (2018, May). Study of Various Data Recovery and Data Back Up Techniques in Cloud Computing & Their Comparison. In 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) (pp. 2021-2024). IEEE.
5. Sudakara, B. B. (2023). Integrating Cloud-Native Testing Frameworks with DevOps Pipelines for Healthcare Applications. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(5), 9309-9316.
6. Chaganti, S. (2022, November). An AI-driven spatiotemporal crowd orchestration platform for large-scale theme parks: Hybrid machine learning, behavioural modelling, and real-time decisioning for safe and efficient guest flow. International Journal on Recent and Innovation Trends in Computing and Communication, 10(11), 275–283.
7. Raja, G. V. (2022). Integrating network forensics with data mining for advanced cybercrime investigation. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(5), 5321-5326.
8. Parasa, M. (2022). Addressing the underutilization of exit interview data: A structured AI-assisted framework for actionable workforce insights in SAP SuccessFactors. Global Scientific and Academic Research Journal of Multidisciplinary Studies, 1(6), 42–52. https://gsarpublishers.com/abstract-2326/
9. Gurram, S. K. (2024). Federated learning for anomaly detection in distributed systems. International Journal of Future Innovative Science and Technology (IJFIST), 7(6), 14031–14040.
10. Kotla, M. R. T. (2023). Autonomous enterprise integration: The future of self-healing data and API ecosystems. International Journal of Research and Applied Innovations (IJRAI), 6(3), 5968–5971.
11. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian journal of science and technology, 8(35), 1-5.
12. Anbazhagan, K., & Sugumar, R. (2016). A proficient two level security contrivances for storing data in cloud. Indian Journal of Science and Technology, 9(48), 1–5. https://doi.org/10.17485/ijst/2016/v9i48/103399
13. Meesala, A. (2022). Adaptive Spread Anomaly Intelligence Framework (ASAIF): A cloud-native AI framework for real-time bid-ask spread anomaly detection and cross-venue liquidity risk intelligence. International Journal of Future Innovative Science and Technology (IJFIST), 5(6), 9597–9604.
14. Navandar, P. (2024). Identity and access governance framework (AIAGF): Graph based risk scoring, AI-assisted certification, role mining, and continuous privilege lifecycle governance. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(1), 10004–10017. https://doi.org/10.15662/IJRPETM.2024.0701012
15. Veershetty, G. (2024). Robust Transition Management From Implementation to Application Management Services. Available at SSRN 6890119.
16. Alex Mathew. (2023). Threat defense through cyber fusion. International Journal of Computer Science and Mobile Computing, 12(1), 24–27. https://doi.org/10.47760/ijcsmc.2022.v12i01.003
17. Prasanna Kumar Natta. (2022). Computer-vision-assisted retail inventory automation: Integrating autonomous scan data with worklist completion systems. International Journal of Science, Research and Technology, 5(6), 8957–8969. https://doi.org/10.15662/IJSRAT.2022.0506009
18. Soundappan, S. J. (2022). Integrated Risk Governance Framework for Financial Compliance Supply Chain Resilience and Enterprise Data Management. International Journal of Computer Technology and Electronics Communication, 5(6), 16254-16263.
19. Dama, H. B. (2024). Cross-Cloud Data Consistency Models for Always-On Banking Platforms. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(4), 8468-8476.
20. Kanchumarthi, S. N. V. P. (2024, April). Hybrid network security architecture: F5–AWS integration, zero-trust enforcement, and SD-WAN for PCI DSS-compliant hybrid environments. World Journal of Advanced Research and Reviews, 22(1), 2111–2117. https://doi.org/10.30574/wjarr.2024.22.1.1162
21. Makkena, B. (2024). Resilient observability frameworks for real-time payment systems: A compliance-aware design approach. Journal of Information Systems Engineering and Management, 9(3).
22. Juvvadi, R. R. (2018). Continuous accounting: Toward a real-time financial reporting architecture for the modern enterprise. Computer Fraud & Security, 2018(12), 33–41.
23. Lanka, S. (2024). Redefining Digital Banking: ANZ’s Pioneering Expansion into Multi-Wallet Ecosystems. International Journal of Technology, Management and Humanities, 10(01), 33-41.
24. Mohammed, S., & Polamarasetty, V. K. (2021). Enterprise multi-cloud transformation and managed services modernization. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 4(9), 2041–2056. https://doi.org/10.15680/IJMRSET.2021.0409023
25. Raja, G. V. (2022). Leveraging Machine Learning for Real-Time Short-Term Snowfall Forecasting Using MultiSource Atmospheric and Terrain Data Integration. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 5(8), 1336-1339.
26. Goel, N. (2023). Zero Trust Architecture: A Revolutionary Approach to Cybersecurity. Res Militaris, Volume 13, Issue 3, pp. 6931–6940.
27. Garg, A. (2022). Using interpretable machine learning identify factors contributing to COVID-19 cases in the United States. In Novel AI and Data Science Advancements for Sustainability in the Era of COVID-19 (pp. 113-158). Academic Press.
28. Chaganti, S. (2022). AI-driven spatiotemporal crowd orchestration platform for large-scale theme parks hybrid machine learning behavioural modelling and real-time decisioning for safe and efficient guest flow. International Journal on Recent and Innovation Trends in Computing and Communication, 10(11), 275–283.
29. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
30. Gopisetty, S. (2023). Who Watches the Cloud Watcher? Building a Team of AI Agents to Continuously Verify Shared Security Controls When a Mid-Sized Bank Can't Trust the SOC Report Alone. European Journal of Advances in Engineering and Technology, 10(10), 165-178.
31. Manda, P. (2023). A Comprehensive Guide to Migrating Oracle Databases to the Cloud: Ensuring Minimal Downtime, Maximizing Performance, and Overcoming Common Challenges. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(3), 8201-8209.