Articles Open Access
Privacy Aware Cloud Intelligence Using Federated Clustering and Explainable AI for Post Quantum Cryptographic Protection
School of Information Technology, University of Pretoria, Pretoria, South Africa
Abstract
The rapid adoption of cloud computing, distributed artificial intelligence, and interconnected enterprise platforms has increased the volume of sensitive information processed across geographically distributed infrastructures. Conventional centralized intelligence approaches require extensive data aggregation, creating privacy, compliance, and security concerns while exposing cloud environments to emerging cryptographic threats from quantum computing. This research proposes a privacy-aware cloud intelligence framework that integrates federated clustering, explainable artificial intelligence (XAI), and post-quantum cryptographic (PQC) protection. The proposed architecture enables participating cloud nodes to collaboratively identify behavioral patterns and security clusters without directly exchanging raw data. Federated clustering is employed to preserve data locality while generating global intelligence from locally derived representations, whereas XAI mechanisms provide interpretable explanations for clustering outcomes, anomalous behaviors, and security decisions. A post-quantum protection layer incorporates quantum-resistant cryptographic mechanisms for securing communication, model exchanges, authentication, and sensitive cloud information against potential quantum-enabled attacks. The methodology combines distributed preprocessing, privacy-preserving feature extraction, local clustering, secure aggregation, explainability analysis, and PQC-based communication. Evaluation considers clustering quality, detection performance, communication overhead, privacy preservation, explanation consistency, and cryptographic efficiency. The framework is designed to support adaptive and trustworthy cloud intelligence while reducing centralized data exposure and improving resilience against future quantum threats.
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Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 9 |
| Number | 6 |
| Pages | 17976-17984 |
| Published | 2025-12-15 |
| Issue | Vol. 9 No. 6 (2025): International Research Journal of Innovative Engineering (IRJIE) |
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32. Hashmi, H., & Srikanth, V. (2023, November). Combining Neural Networks to Recognize Offline Digits & Words by Training the Model Using Keras. In 2023 3rd International Conference on Advancement in Electronics & Communication Engineering (AECE) (pp. 694-697). IEEE.
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2. Ambalakannu, M. (2025, November). Next-Gen Healthcare Claims Optimization: DL-Based ResAttBiL Integrated with CDC, Modular Design, and Data Observability. In 2025 5th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS) (pp. 980-985). IEEE.
3. Kavuru, R. R., & Balaji, R. (2023). Extending the service mesh into a compliance enforcement fabric: A policy-aware architecture for regulated financial platforms. International Journal of Emerging Trends in Engineering and Management Research, 8(3), 13612–13618.
4. Bitragunta, S. L. V. (2025). AI-assisted stability analysis of microgrids with distributed renewable energy sources. International Journal of Advanced Engineering Science and Information Technology, 8(1), 15681–15686.
5. Polamarasetty, V. K. (2023). Modern enterprise HR technology through Workday-based benefits and absence management. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(4), 6908–6913.
6. Gaddam, M. K., Kapoor, S., Vedula, J., Manu, M., Usmani, M. A., & Polvanov, S. (2025, July). LiDAR Sensor Simulation in Unity: Real-Time Performance for Autonomous Systems and Virtual Environments. In 2025 International Conference on Information, Implementation, and Innovation in Technology (I2ITCON) (pp. 1-6). IEEE.
7. Ramasamy, M. (2022). Closed-loop network automation: Architectural principles from Catalyst Center-scale systems. International Research Journal of Innovative Engineering, 6(3), 10698–10708.
8. Badam, L. R. (2023). Explainable AI framework for real-time financial fraud detection in banking systems. International Journal of Emerging Trends in Engineering and Management Research, 8(2), 13241–13251.
9. Pothuri, M. K. (2025). Building Self-Service BI in the Cloud with AI Integration: Power BI and Snowflake. International Journal of Emerging Trends in Computer Science and Information Technology, 256-262.
10. Sasikumar, B., Venkatasalam, K., & Rajendran, P. (2024). Induction motor fault detection and classification using rcnn and surf based machine learning algorithms and infrared thermography. Scientia Iranica.
11. Sugumar, R. (2022). Federated Learning and Distributed AI Architectures for Cloud-Based Cyber Healthcare Data Collaboration Systems. International Journal of Future Innovative Science and Technology (IJFIST), 5(5), 9232.
12. Patel, K., Hariharan, A., & Sucharitha, R. (2025, August). Implementing Next-Gen Predictive Maintenance in Industrial Machineries: A Comparative Analysis of Small, Medium & Large Enterprises. In 2025 IEEE Technology and Engineering Management Society Conference-Global (TEMSCON Global) (pp. 1-3). IEEE.
13. Soundappan, S. J. (2021). Integrated Artificial Intelligence Framework for Enterprise Cloud Modernization and Intelligent Threat Management Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 4(4), 5274-5284.
14. Mathew, A. (2021). Artificial intelligence and cognitive computing for 6G communications & networks. International Journal of Computer Science and Mobile Computing, 10(3), 26-31.
15. Kavuru, R. R., & Balaji, R. (2023). Extending the service mesh into a compliance enforcement fabric: A policy-aware architecture for regulated financial platforms. International Journal of Emerging Trends in Engineering and Management Research, 8(3), 13612–13618.
16. Chinnam, N. B. (2023). Modernizing retail fulfillment operations through automated load execution and microservices. International Journal of Computer Technology and Electronics Communication, 6(4), 7367–7371.
17. Karakondu, M., Jambagi, G., & Tatavarthi, S. (2025). Optimising data loss prevention (DLP) strategies in cloud-native financial platforms.
18. Reddy, K. V. (2025). Layered Verification Strategies for AI Accelerator Hardware: Matrix Engines and SRAM Buffers. Journal of Computer Science and Technology Studies, 7(5), 786-795.
19. 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.
20. Ahuja, D. (2025, August). Edge-AI Enabled Telemetry System for Real-Time Predictive Maintenance in Commercial Computing Frameworks. In 2025 Global Conference on Information Technology and Communication Networks (GITCON) (pp. 1-7). IEEE.
21. Gopinathan, V. R. (2023). Modernizing Enterprise Applications Using Intelligent API Frameworks Machine Learning and Cloud Native Computing. International Journal of Science, Research and Technology, 6(5), 10690-10697.
22. Kondapalli, K. K., & Gunupudi, C. (2023). Artificial intelligence ethics and principles in public sector healthcare: A governance framework for responsible AI adoption in local and sub-national health services. Lex localis-Journal of Local Self-Government, 21(S2), 61–81.
23. Kundavaram, R. R., Bandhela, R. R., & Onteddu, A. R. (2022). AI-driven predictive modeling in healthcare: A data science perspective on U.S. healthcare data. South Eastern European Journal of Public Health. https://doi.org/10.70135/seejph.vi.6691
24. 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.
25. Chinnam, N. B. (2023). Modernizing retail fulfillment operations through automated load execution and microservices. International Journal of Computer Technology and Electronics Communication, 6(4), 7367–7371.
26. Khare, A., Khare, S., Goel, O., & Goel, P. (2024). Strategies for successful organizational change management in large digital transformation. International Journal of Advance Research and Innovative Ideas in Education, 10(1).
27. Bellundagi, M. (2022). Performance Optimization Techniques for Enterprise Java Applications Using Middleware and Messaging Systems. International Journal of Computer Technology and Electronics Communication, 5(3), 5158-5168.
28. 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.
29. Selvarajan, K. (2025). Petabyte-scale data processing for real-time enterprise security analytics. International Research Journal of Innovative Engineering (IRJIE), 9(5), 17566–17576.
30. Raja, G. V. (2022). AI Enabled Cloud and Data Engineering Frameworks for Secure, Scalable, and Intelligent Cyber Physical Analytics Systems. International Journal of Research and Applied Innovations, 5(4), 7395-7409.
31. Mudunuri, L. N. R., Aragani, V. M., & Maroju, P. K. (2024, December). Development of an AI-Powered Garbage Detection System for Environmental Sustainability Via YOLOv5. In International Conference on Information and Communication Technology for Competitive Strategies (pp. 317-327). Singapore: Springer Nature Singapore.
32. Hashmi, H., & Srikanth, V. (2023, November). Combining Neural Networks to Recognize Offline Digits & Words by Training the Model Using Keras. In 2023 3rd International Conference on Advancement in Electronics & Communication Engineering (AECE) (pp. 694-697). IEEE.
33. Matrouk, K., V, S., Kumar, S., Bhadla, M. K., Sabirov, M., & Saadh, M. J. (2023). Deep Learning–based Dynamic User Alignment in Social Networks. ACM Journal of Data and Information Quality, 15(3), 1-26.
34. Long, Z., Yan, H., Shen, G., Zhang, X., He, H., & Cheng, L. (2024). A Transformer-based network intrusion detection approach for cloud security. Journal of Cloud Computing, 13, 5. https://doi.org/10.1186/s13677-023-00574-9
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