Investigating Security Enhancement in Hybrid Clouds via a Blockchain-Fused Privacy Preservation Strategy: Pilot Study

Authors

  • Tabitha Chukwudi Aghaunor Robert Morris University
  • Eferhire Valentine Ugbotu University of Salford
  • Emeke Ugboh Federal College of Education (Technical) Asaba
  • Paul Avwerosuoghene Onoma Federal University of Petroleum Resources
  • Frances Uchechukwu Emordi Dennis Osadebay University
  • Arnold Adimabua Ojugo Federal University of Petroleum Resources https://orcid.org/0000-0003-4150-5163
  • Victor Ochuko Geteloma Federal University of Petroleum Resources
  • Rebecca Okeoghene Idama Southern Delta University
  • Peace Oguguo Ezzeh Federal College of Education (Technical) Asaba

DOI:

https://doi.org/10.62411/jcta.15508

Keywords:

Blockchain identity management, Differential privacy, Homomorphic encryption, Multi-cloud security, Privacy-preserving computation, Secure multi-party computation, Sustainable digital infrastructure, Zero-Trust Architecture

Abstract

The proliferation of cloud infrastructures has intensified concerns regarding data security, integrity, identity and access management, and user privacy. Despite recent advances, existing solutions often lack comprehensive integration of privacy-preserving mechanisms, dynamic trust management, and cross-provider interoperability. This study proposes an AI-enabled, zero-trust, blockchain-fused identity management framework for secure, privacy-preserving multi-cloud environments. The framework integrates homomorphic encryption with differential privacy for aggregate-level protection and secure multi-party computation for collaborative data processing. The proposed system was validated in a simulated multi-cloud environment using CloudSim, Ethereum blockchain, and AWS EC2. Experimental results indicate homomorphic encryption latency of approximately 450ms per operation and statistically significant security improvements (t(128) = 12.47, p < 0.001), privacy (t(95) = 8.93, p < 0.001), and throughput (t(156) = 15.21, p < 0.001). The framework achieved differential privacy with ε = 0.1 while retaining 99.2% data utility, and demonstrated a 34% improvement in processing speed over conventional differential privacy approaches. In addition, the implementation was observed to be 2.3× faster than BGV-based configurations, with 45% lower memory consumption than CKKS and a 67% reduction in ciphertext size relative to baseline implementations. From an operational perspective, the framework shows a 23% reduction in security management costs, a 31% improvement in resource utilization efficiency, and an 18% decrease in compliance audit expenses. The model further indicates a 27% reduction in total cost of ownership (TCO) compared with multi-vendor security solutions, a projected return on investment (ROI) within 14 months, and an 89% reduction in security incident response costs under the evaluated conditions.

Author Biographies

Tabitha Chukwudi Aghaunor, Robert Morris University

School of Data Intelligence and Technology, Robert Morris University, Pittsburgh, PA15108, United States of America

Eferhire Valentine Ugbotu, University of Salford

Faculty of Science, Engineering and Environment, University of Salford, Manchester M54WT, United Kingdom

Emeke Ugboh, Federal College of Education (Technical) Asaba

School of Science Education, Federal College of Education (Technical) Asaba, Delta State 320212, Nigeria

Paul Avwerosuoghene Onoma, Federal University of Petroleum Resources

College of Computing, Federal University of Petroleum Resources, Effurun, Delta State 330102, Nigeria

Frances Uchechukwu Emordi, Dennis Osadebay University

Faculty of Computing, Dennis Osadebay University, Asaba, Delta State 320212, Nigeria

Arnold Adimabua Ojugo, Federal University of Petroleum Resources

College of Computing, Federal University of Petroleum Resources, Effurun, Delta State 330102, Nigeria

Victor Ochuko Geteloma, Federal University of Petroleum Resources

College of Computing, Federal University of Petroleum Resources, Effurun, Delta State 330102, Nigeria

Rebecca Okeoghene Idama, Southern Delta University

Faculty of Computing, Southern Delta University, Ozoro, Delta State 334111, Nigeria

Peace Oguguo Ezzeh, Federal College of Education (Technical) Asaba

School of Science Education, Federal College of Education (Technical) Asaba, Delta State 320212, Nigeria

References

O. Ali, A. Shrestha, V. Osmanaj, and S. Muhammed, “Cloud computing technology adoption: an evaluation of key factors in local governments,” Inf. Technol. People, vol. 34, no. 2, pp. 666–703, Apr. 2020, doi: 10.1108/ITP-03-2019-0119.

D. R. I. M. Setiadi et al., “Hyperchaotic cross-coupled quantum 2D maps with interdependent rotational asymmetry for secure image encryption,” Opt. Commun., vol. 600, no. September 2025, p. 132699, Mar. 2026, doi: 10.1016/j.optcom.2025.132699.

K. J. Merseedi and D. S. R. M. Zeebaree, “Cloud Architectures for Distributed Multi-Cloud Computing: A Review of Hybrid and Federated Cloud Environment,” Indones. J. Comput. Sci., vol. 13, no. 2, Apr. 2024, doi: 10.33022/ijcs.v13i2.3811.

E. Akinrintoyo, V. R. Garate, and P. Bremner, “User-Centered Design of Internet of Robotic Things (IoRT) for People Living with Dementia,” Int. J. Soc. Robot., 2025, doi: 10.1007/s12369-025-01261-2.

M. I. Akazue, I. A. Debekeme, A. E. Edje, C. Asuai, and U. J. Osame, “UNMASKING FRAUDSTERS: Ensemble Features Selection to Enhance Random Forest Fraud Detection,” J. Comput. Theor. Appl., vol. 1, no. 2, pp. 201–211, Dec. 2023, doi: 10.33633/jcta.v1i2.9462.

F. O. Aghware, R. E. Yoro, P. O. Ejeh, C. C. Odiakaose, F. U. Emordi, and A. A. Ojugo, “DeLClustE: Protecting Users from Credit-Card Fraud Transaction via the Deep-Learning Cluster Ensemble,” Int. J. Adv. Comput. Sci. Appl., vol. 14, no. 6, pp. 94–100, 2023, doi: 10.14569/IJACSA.2023.0140610.

A. Kaiser, E. Wageneder, and C. Kerschbaum, “Advanced Spiritual Knowledge Management: Main Features of the Concept and Initial Ideas for Implementation in Schools and School Pastoral Care,” Eur. Conf. Knowl. Manag., vol. 26, no. 1, pp. 499–506, 2025, doi: 10.34190/eckm.26.1.3810.

A. A. Ojugo and A. O. Eboka, “Empirical Bayesian network to improve service delivery and performance dependability on a campus network,” IAES Int. J. Artif. Intell., vol. 10, no. 3, p. 623, Sep. 2021, doi: 10.11591/ijai.v10.i3.pp623-635.

H. Li, X. Yang, H. Wang, W. Wei, and W. Xue, “A Controllable Secure Blockchain-Based Electronic Healthcare Records Sharing Scheme,” J. Healthc. Eng., vol. 2022, pp. 1–11, Mar. 2022, doi: 10.1155/2022/2058497.

S. Ghosh, S. K. Verma, U. Ghosh, and M. Al-Numay, “Improved End-to-End Data Security Approach for Cloud Computing,” Sustainability, vol. 15, no. 22, p. 16010, Nov. 2023, doi: 10.3390/su152216010.

M. A. Haque et al., “Cybersecurity in Universities: An Evaluation Model,” SN Comput. Sci., vol. 4, no. 5, p. 569, Jul. 2023, doi: 10.1007/s42979-023-01984-x.

P. Manickam et al., “Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare,” Biosensors, vol. 12, no. 8, p. 562, Jul. 2022, doi: 10.3390/bios12080562.

A. H. Allam, I. Gomaa, H. H. Zayed, and M. Taha, “IoT-based eHealth using blockchain technology: a survey,” Cluster Comput., vol. 0123456789, 2024, doi: 10.1007/s10586-024-04357-y.

J. Jose, D. Rivera, W. Akbar, T. A. Khan, and A. Muhammad, “Secure Enrollment Token Delivery Mechanism for Zero Trust Networks Using Secure enrollment token delivery mechanism for Zero Trust networks using blockchain ‡,” no. July, 2023, doi: 10.1587/trans.E0.

S. Quamara and A. K. Singh, “An In-depth Security and Performance Investigation in Hyperledger Fabric-configured Distributed Computing Systems,” Blockchain Model., vol. 1, no. 1, pp. 12–24, 2023.

A. J. Chukwunalu, T. F. Uketui, and M. Eleanya, “Cybersecurity risk assessment for non-experts - focusing on small and medium enterprises,” IOSR J. Comput. Eng., vol. 26, no. 2, pp. 27–37, 2024, doi: 10.9790/0661-2602022737.

A. Şentürk and S. Terazi, “IoT security with blockchain: A review,” Eur. J. Res. Dev., vol. 3, no. 4, pp. 117–132, 2023, doi: 10.56038/ejrnd.v3i4.370.

A. S. Khan et al., “Blockchain-Based Lightweight Multifactor Authentication for Cell-Free in Ultra-Dense 6G-Based (6-CMAS) Cellular Network,” IEEE Access, vol. 11, no. January, pp. 20524–20541, 2023, doi: 10.1109/ACCESS.2023.3249969.

H. H. Ou, C. H. Pan, Y. M. Tseng, and I. C. Lin, “Decentralized Identity Authentication Mechanism: Integrating FIDO and Blockchain for Enhanced Security,” Appl. Sci., vol. 14, no. 9, 2024, doi: 10.3390/app14093551.

I. D. Ukadike, M. I. Akazue, E. U. Omede, and T. . Akpoyibo, “Development of an IoT based Air Quality Monitoring System,” Int. J. Innov. Technol. Explor. Eng., vol. 7, no. 4, pp. 53–62, Sep. 2023, doi: 10.35940/ijitee.J1004.08810S19.

S. Sinha, “Blockchain for Enhancing IoT Privacy and Security,” Int. J. Innov. Res. Comput. Sci. Technol., vol. 12, no. 2, pp. 106–110, Mar. 2024, doi: 10.55524/ijircst.2024.12.2.18.

A. P. Binitie, D. N. Akhator, and K. K. Chukwubueze, “Design of a Resilient System against Shoulder Surfing Attack : Adaptable to USSD Channel,” Res. Sq., pp. 1–19, 2023, doi: 10.21203/rs.3.rs-2793844/v1 License:

S. Abdul Hannan, “a Blockchain Technology and Internet of Things To Secure in Healthcare System,” J. Adv. Res. Comput. Sci. Eng. (ISSN 2456-3552), vol. 9, no. 4, pp. 12–19, 2023, doi: 10.53555/nncse.v9i4.1641.

S. V. Bayani, S. Prakash, and L. Shanmugam, “Data Guardianship: Safeguarding Compliance in AI/ML Cloud Ecosystems,” J. Knowl. Learn. Sci. Technol. ISSN 2959-6386, vol. 2, no. 3, pp. 436–456, Sep. 2023, doi: 10.60087/jklst.vol2.n3.p456.

F. Al-Turjman, H. Zahmatkesh, and L. Mostarda, “Quantifying uncertainty in internet of medical things and big-data services using intelligence and deep learning,” IEEE Access, vol. 7, pp. 115749–115759, 2019, doi: 10.1109/ACCESS.2019.2931637.

C. Nartey et al., “On Blockchain and IoT Integration Platforms: Current Implementation Challenges and Future Perspectives,” Wirel. Commun. Mob. Comput., vol. 2021, pp. 1–25, Apr. 2021, doi: 10.1155/2021/6672482.

D. A. Zetzsche, D. W. Arner, and R. P. Buckley, “Decentralized Finance,” J. Financ. Regul., vol. 6, no. 2, pp. 172–203, Sep. 2020, doi: 10.1093/jfr/fjaa010.

B. O. Malasowe, F. O. Aghware, M. D. Okpor, B. E. Edim, R. E. Ako, and A. A. Ojugo, “Techniques and Best Practices for Handling Cybersecurity Risks in Educational Technology Environment ( EdTech ),” J. Sci. Technol. Res., vol. 6, no. 2, pp. 293–311, 2024, doi: 10.5281/zenodo.12617068.

R. E. Yoro, F. O. Aghware, B. O. Malasowe, O. Nwankwo, and A. A. Ojugo, “Assessing contributor features to phishing susceptibility amongst students of petroleum resources varsity in Nigeria,” Int. J. Electr. Comput. Eng., vol. 13, no. 2, p. 1922, Apr. 2023, doi: 10.11591/ijece.v13i2.pp1922-1931.

M. Bishop, C. Gates, D. Frincke, and F. L. Greitzer, “AZALIA: An A to Z assessment of the likelihood of insider attack,” 2009 IEEE Conf. Technol. Homel. Secur. HST 2009, pp. 385–392, 2009, doi: 10.1109/THS.2009.5168063.

R. O. Z. Dwi and Y. Asriningtias, “Real-Time Location Monitoring and Routine Reminders Based on Internet of Things Integrated with Mobile for Dementia Disorder,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 9, no. 1, pp. 77–84, Jan. 2025, doi: 10.29207/resti.v9i1.6105.

V. V. Krishna, Y. Rupa, G. Koushik, T. Varun, B. V. Kiranmayee, and K. Akhil, “A Comparative Study on Authentication Vulnerabilities and Security Issues in Wearable Devices,” Proc. Fourth Int. Conf. Adv. Comput. Eng. Commun. Syst. (ICACECS 2023), Atl. Highlights Comput. Sci. 18, vol. 18, no. Icacecs, pp. 106–116, 2023, doi: 10.2991/978-94-6463-314-6_11.

A. Adimabua Ojugo, P. Ogholuwarami Ejeh, O. Chukwufunaya Christopher, A. Okonji Eboka, and F. Uchechukwu Emordi, “Improved distribution and food safety for beef processing and management using a blockchain-tracer support framework,” Int. J. Informatics Commun. Technol., vol. 12, no. 3, p. 205, Dec. 2023, doi: 10.11591/ijict.v12i3.pp205-213.

I. Odun-Ayo, V. Geteloma, S. Misra, R. Ahuja, and R. Damasevicius, “Systematic Mapping Study of Utility-Driven Platforms for Clouds,” 2020, pp. 762–774. doi: 10.1007/978-3-030-30577-2_68.

O. Ahmad et al., “Mechanism for Securing Smart Cities,” Sensors, vol. 23, 2023.

Samuel Onimisi Dawodu, Adedolapo Omotosho, Odunayo Josephine Akindote, Abimbola Oluwatoyin Adegbite, and Sarah Kuzankah Ewuga, “Cybersecurity Risk Assessment in Banking: Methodologies and Best Practices,” Comput. Sci. IT Res. J., vol. 4, no. 3, pp. 220–243, 2023, doi: 10.51594/csitrj.v4i3.659.

E. Marasco, M. Albanese, V. V. R. Patibandla, A. Vurity, and S. S. Sriram, “Biometric multi‐factor authentication: On the usability of the FingerPIN scheme,” Secur. Priv., vol. 6, no. 1, 2023, doi: 10.1002/spy2.261.

N. Rehman, “Strengthening Financial Institutions ’ Data Security with Blockchain Technology and Zero Trust Security : A Comprehensive Cyber Defense Strategy Date : November , 2024,” Research Gate. 2024. doi: 10.13140/RG.2.2.21956.85127.

A. O. Eboka and A. A. Ojugo, “Mitigating technical challenges via redesigning campus network for greater efficiency, scalability and robustness: A logical view,” Int. J. Mod. Educ. Comput. Sci., vol. 12, no. 6, pp. 29–45, 2020, doi: 10.5815/ijmecs.2020.06.03.

W. Li, S. Manickam, Y. Chong, and S. Karuppayah, “Talking Like a Phisher: LLM-Based Attacks on Voice Phishing Classifiers,” arXiv, no. July. Jul. 22, 2025. [Online]. Available: http://arxiv.org/abs/2507.16291

L. Deon and T. Best, “Zero Trust Security and Cloud Security : A Modern Approach to Cyberattack Prevention Date : February , 2025,” ResearchGate, vol. 1, no. February, 2025, doi: 10.13140/RG.2.2.24739.57126.

A. Salam et al., “Securing Smart Manufacturing by Integrating Anomaly Detection with Zero-Knowledge Proofs,” IEEE Access, vol. 12, pp. 36346–36360, 2024, doi: 10.1109/ACCESS.2024.3373697.

M. Uddin, K. Salah, R. Jayaraman, S. Pesic, and S. Ellahham, “Blockchain for drug traceability: Architectures and open challenges,” Health Informatics J., vol. 27, no. 2, p. 146045822110112, Apr. 2021, doi: 10.1177/14604582211011228.

A. P. Binitie and O. J. Babatunde, “Evaluating the privacy issues, potential risks, and security measures associated with using social media platforms,” Int. J. African Res. Sustain. Stud., vol. 3, no. 2, pp. 167–179, 2024.

J. Jose Diaz Rivera, A. Muhammad, and W.-C. Song, “Securing Digital Identity in the Zero Trust Architecture: A Blockchain Approach to Privacy-Focused Multi-Factor Authentication,” IEEE Open J. Commun. Soc., vol. 5, pp. 2792–2814, 2024, doi: 10.1109/OJCOMS.2024.3391728.

J. Wu et al., “SPCL: A Smart Access Control System That Supports Blockchain,” Appl. Sci., vol. 14, no. 7, p. 2978, Apr. 2024, doi: 10.3390/app14072978.

A. O. Eboka et al., “Pilot study on deploying a wireless sensor-based virtual-key access and lock system for home and industrial frontiers,” Int. J. Informatics Commun. Technol., vol. 14, no. 1, p. 287, Apr. 2025, doi: 10.11591/ijict.v14i1.pp287-297.

J. Cena, “Multi-Factor Authentication Paradigms for Securing Industrial Internet of Multi-Factor Authentication Paradigms for Securing Industrial Internet of Things (IIoT) Assets,” Bull. Electr. Eng. Informatics, vol. 21, no. May, pp. 23–46, 2024.

G. N. Brijwani et al., “HealthShield: A Blockchain-Based Electronic Health Recording System with Enhanced Security Algorithm for Immutable and Confidential Health Data Management,” Int. J. Sci. Res. Sci. Technol., vol. 11, no. 3, pp. 794–814, 2024, doi: 10.32628/ijsrst24113234.

K. G. Arachchige, P. Branch, and J. But, “An Analysis of Blockchain-Based IoT Sensor Network Distributed Denial of Service Attacks,” Sensors, vol. 24, no. 10, p. 3083, May 2024, doi: 10.3390/s24103083.

M. A. Aleisa, C. Science, C. Computer, and I. Sciences, “Blockchain-Enabled Zero Trust Architecture for Privacy-Preserving Cybersecurity in IoT Environments,” IEEE Access, vol. PP, p. 1, 2025, doi: 10.1109/ACCESS.2025.3529309.

Y. Lu, X. Huang, Y. Dai, S. Maharjan, and Y. Zhang, “Blockchain and Federated Learning for Privacy-Preserved Data Sharing in Industrial IoT,” IEEE Trans. Ind. Informatics, vol. 16, no. 6, pp. 4177–4186, Jun. 2020, doi: 10.1109/TII.2019.2942190.

R. E. Yoro et al., “Adaptive DDoS detection mode in software-defined SIP-VoIP using transfer learning with boosted meta-learner,” PLoS One, vol. 20, no. 6, p. e0326571, Jun. 2025, doi: 10.1371/journal.pone.0326571.

S. Bamashmos, N. Chilamkurti, and A. S. Shahraki, “Two-Layered Multi-Factor Authentication Using Decentralized Blockchain in an IoT Environment,” Sensors, vol. 24, no. 11, 2024, doi: 10.3390/s24113575.

P. O. Ejeh et al., “Data-Driven Framework for Strategic Knowledge Management to Enhance Organizational Learning : A Pilot Study,” J. Behav. Informatics, Digit. Humanit. Dev. Res., vol. 11, no. 4, pp. 11–36, 2025, doi: 10.22624/AIMS/BHI/V11N4P2.

F. O. Aghware et al., “BloFoPASS: A blockchain food palliatives tracer support system for resolving welfare distribution crisis in Nigeria,” Int. J. Informatics Commun. Technol., vol. 13, no. 2, p. 178, Aug. 2024, doi: 10.11591/ijict.v13i2.pp178-187.

K. Okeke and S. Omojola, “Enhancing Cybersecurity Measures in Critical Infrastructure: Challenges and Innovations for Resilience,” J. Sci. Res. Reports, vol. 31, no. 2, pp. 474–484, Mar. 2025, doi: 10.9734/jsrr/2025/v31i22868.

W. J. Sheng, I. F. Kasmin, S. Amin, and N. K. Zainal, “Addressing user perception and implementing Hedera Hashgraph and voice recognition into Multi-Factor Authentication (MFA) system,” Int. J. Data Sci. Adv. Anal., vol. 4, pp. 194–201, 2023, doi: 10.69511/ijdsaa.v4i0.165.

U. Qureshi, B. Doshi, A. More, K. Joshi, and K. Kumar, “Integrating Fully Homomorphic Encryption and Zero-Knowledge Proofs for Efficient Verifiable Computation,” J. Comput. Theor. Appl., vol. 3, no. 3, pp. 274–285, Feb. 2026, doi: 10.62411/jcta.14181.

R. J. van Geest, G. Cascavilla, J. Hulstijn, and N. Zannone, “The applicability of a hybrid framework for automated phishing detection,” Comput. Secur., vol. 139, no. January, p. 103736, Apr. 2024, doi: 10.1016/j.cose.2024.103736.

T. Suleski and M. Ahmed, “A Data Taxonomy for Adaptive Multifactor Authentication in the Internet of Health Care Things,” J. Med. Internet Res., vol. 25, pp. 1–22, 2023, doi: 10.2196/44114.

A. Ibor, M. Hooper, C. Maple, J. Crowcroft, and G. Epiphaniou, “Considerations for trustworthy cross-border interoperability of digital identity systems in developing countries,” AI Soc., no. August, Aug. 2024, doi: 10.1007/s00146-024-02008-9.

P. V. Kakarlapudi and Q. H. Mahmoud, “Design and Development of a Blockchain-Based System for Private Data Management,” Electronics, vol. 10, no. 24, p. 3131, Dec. 2021, doi: 10.3390/electronics10243131.

A. D. Dwivedi, G. Srivastava, S. Dhar, and R. Singh, “A decentralized privacy-preserving healthcare blockchain for IoT,” Sensors (Switzerland), vol. 19, no. 2, pp. 1–17, 2019, doi: 10.3390/s19020326.

R. Karanjai et al., “Decentralized Translator of Trust: Supporting Heterogeneous TEE for Critical Infrastructure Protection,” Aug. 2023, doi: 10.1145/3594556.3594626.

X. Zhang, Q. Wang, R. Li, and Q. Wang, “Frontrunning Block Attack in PoA Clique: A Case Study,” IEEE Int. Conf. Blockchain Cryptocurrency, ICBC 2022, pp. 1–7, 2022, doi: 10.1109/ICBC54727.2022.9805543.

J. A. Aparecido Cardoso, F. T. Ishizu, J. T. De Lima, and J. D. S. Pinto, “Blockchain Based MFA Solution: the use of hydro raindrop MFA for information security on WordPress websites,” Brazilian J. Oper. Prod. Manag., vol. 16, no. 2, pp. 281–293, May 2019, doi: 10.14488/BJOPM.2019.v16.n2.a9.

D. C. Nguyen et al., “Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges,” IEEE Internet Things J., vol. 8, no. 16, pp. 12806–12825, Aug. 2021, doi: 10.1109/JIOT.2021.3072611.

G. Habib, S. Sharma, S. Ibrahim, I. Ahmad, S. Qureshi, and M. Ishfaq, “Blockchain Technology: Benefits, Challenges, Applications, and Integration of Blockchain Technology with Cloud Computing,” Futur. Internet, vol. 14, no. 11, p. 341, Nov. 2022, doi: 10.3390/fi14110341.

N. Afrin and A. Pathak, “Blockchain-Powered Security and Transparency in Supply Chain: Exploring Traceability and Authenticity through Smart Contracts,” Int. J. Comput. Appl., vol. 185, no. 49, pp. 975–8887, 2023, doi: 10.5120/ijca2023923318.

S. Jabbar, H. Lloyd, M. Hammoudeh, B. Adebisi, and U. Raza, “Blockchain-enabled supply chain: analysis, challenges, and future directions,” Multimed. Syst., vol. 27, no. 4, pp. 787–806, Aug. 2021, doi: 10.1007/s00530-020-00687-0.

V. Geteloma, C. K. Ayo, and R. N. Goddy-Wurlu, “A Proposed Unified Digital Id Framework for Access to Electronic Government Services,” J. Phys. Conf. Ser., vol. 1378, no. 4, p. 042039, Dec. 2019, doi: 10.1088/1742-6596/1378/4/042039.

A. Jaber and L. Fritsch, “Towards AI-powered Cybersecurity Attack Modeling with Simulation Tools: Review of Attack Simulators,” Lect. Notes Networks Syst., vol. 571 LNNS, no. October, pp. 249–257, 2023, doi: 10.1007/978-3-031-19945-5_25.

M. Ifeanyi Akazue et al., “FiMoDeAL: pilot study on shortest path heuristics in wireless sensor network for fire detection and alert ensemble,” Bull. Electr. Eng. Informatics, vol. 13, no. 5, pp. 3534–3543, Oct. 2024, doi: 10.11591/eei.v13i5.8084.

M. Jagadeeswari, P. N. Karthi, V. A. Nitish Kumar, and S. L. S. Ram, “A Secure File Sharing and Audit Trail Tracking Platform with Advanced Encryption Standard for Cloud-Based Environments,” in 2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC), Jul. 2023, pp. 540–547. doi: 10.1109/ICESC57686.2023.10193389.

S. F. Okumaya, “Analytic Approaches To Detect Insider Threats,” Softw. Eng. Inst., vol. 12, pp. 1–50, 2022.

Z. Zhang et al., “Comprehensive landscape of immune-based classifier related to early diagnosis and macrophage M1 in spinal cord injury,” Aging (Albany. NY)., vol. 15, no. 4, pp. 1–19, 2023, doi: 10.18632/aging.204548.

P. K. Yadalam, S. B. Shenoy, R. V. Anegundi, S. A. Mosaddad, and A. Heboyan, “Advanced machine learning for estimating vascular occlusion percentage in patients with ischemic heart disease and periodontitis,” Int. J. Cardiol. Cardiovasc. Risk Prev., vol. 21, no. May, pp. 0–3, 2024, doi: 10.1016/j.ijcrp.2024.200291.

Rehana Sultana Khan, “Security challenges and mitigation strategies in multi-cloud environments: A comprehensive analysis,” World J. Adv. Res. Rev., vol. 26, no. 1, pp. 3725–3731, Apr. 2025, doi: 10.30574/wjarr.2025.26.1.1502.

M. Jameaba, “Digitization, FinTech Disruption, and Financial Stability: The Case of the Indonesian Banking Sector,” SSRN Electron. J., vol. 34, pp. 1–44, 2020, doi: 10.2139/ssrn.3529924.

P. Radanliev and D. De Roure, “Advancing the cybersecurity of the healthcare system with self-optimising and self-adaptative artificial intelligence (part 2),” Health Technol. (Berl)., vol. 12, no. 5, pp. 923–929, 2022, doi: 10.1007/s12553-022-00691-6.

O. O. Olaniyi, O. J. Okunleye, S. O. Olabanji, C. U. Asonze, and S. A. Ajayi, “IoT Security in the Era of Ubiquitous Computing: A Multidisciplinary Approach to Addressing Vulnerabilities and Promoting Resilience,” Asian J. Res. Comput. Sci., vol. 16, no. 4, pp. 354–371, 2023, doi: 10.9734/ajrcos/2023/v16i4397.

D. R. I. M. Setiadi et al., “Single Qubit Quantum Logistic-Sine XYZ-Rotation Maps: An Ultra-Wide Range Dynamics for Image Encryption,” Comput. Mater. Contin., vol. 83, no. 2, pp. 2161–2188, 2025, doi: 10.32604/cmc.2025.063729.

H. A. Abdulmalik and A. A. Yassin, “Secure two-factor mutual authentication scheme using shared image in medical healthcare environment,” Bull. Electr. Eng. Informatics, vol. 12, no. 4, pp. 2474–2483, 2023, doi: 10.11591/eei.v12i4.4459.

J. Northrop, “Interoperability Challenges And Solutions In Multi-Vendor Iot Ecosystems For Agriculture,” Research Gate. 2025.

Downloads

Published

2026-02-24

How to Cite

Aghaunor, T. C., Ugbotu, E. V., Ugboh, E., Onoma, P. A., Emordi, F. U., Ojugo, A. A., Geteloma, V. O., Idama, R. O., & Ezzeh, P. O. (2026). Investigating Security Enhancement in Hybrid Clouds via a Blockchain-Fused Privacy Preservation Strategy: Pilot Study. Journal of Computing Theories and Applications, 3(4), 428–442. https://doi.org/10.62411/jcta.15508