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International Journal of Modern Computer Science and IT Innovations

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Machine-Learning Architectures enabling Human Trait Verification Alternatives within Risk-Coverage Ecosystems: Resilient Identity Validation, Policy Adherence

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Dr. Rohan Verma 1 , Dr. Sneha Kulkarni 1 ,
4 Department of Computer Science and Artificial Intelligence Global Institute of Technology and Innovation (GITI) Pune, Maharashtra, India
4 Department of Cloud Computing and Smart Healthcare Systems Advanced Research University (ARU) Chennai, Tamil Nadu, India

Abstract

The increasing reliance on digital infrastructures in risk-coverage ecosystems such as insurance, healthcare financing, and financial protection services has necessitated robust identity verification mechanisms. Traditional authentication approaches, including password-based systems and static biometric identifiers, are increasingly vulnerable to adversarial manipulation, data breaches, and regulatory non-compliance. This study proposes a comprehensive analytical framework that integrates machine-learning-driven human trait verification architectures as resilient alternatives for identity validation within risk-coverage environments. The research synthesizes advancements in large language models, retrieval-augmented generation (RAG), secure access control models, and edge-cloud computing paradigms to establish a multi-layered verification ecosystem.

The proposed framework emphasizes adaptive identity validation using physiological, behavioral, and contextual trait inference mechanisms enhanced by machine learning. It incorporates zero-trust architectures, attribute-based access control (ABAC), and cryptographic protocols to ensure secure, policy-compliant operations. Furthermore, the study examines the implications of generative AI in identity modeling, particularly addressing hallucination risks, privacy vulnerabilities, and synthetic data utilization. The integration of cloud-edge-end intelligence enables scalable deployment while maintaining real-time verification capabilities.

Through a critical synthesis of existing literature and conceptual modeling, the study identifies key challenges, including model interpretability, regulatory compliance (e.g., GDPR and HIPAA), adversarial robustness, and ethical concerns in automated identity systems. The findings highlight that hybrid architectures combining machine learning, cryptographic assurance, and regulatory alignment significantly enhance system resilience. The research contributes to the development of next-generation identity verification systems that are secure, adaptive, and policy-compliant, thereby strengthening trust and operational integrity within risk-coverage ecosystems.

Keywords

Machine Learning, Identity Verification, Risk-Coverage Systems, Retrieval-Augmented Generation

References

A, Patil Rachana Yogesh, and D. S. R. B. “Formal Verification of Secure Evidence Collection Protocol using BAN Logic and AVISPA ” Procedia Computer Science 167 : 1334–1344, 2020.
E. Bou-Harb, C. Fachkha, M. Pourzandi, M. Debbabi and C. Assi, “Communication security for smart grid distribution networks,” in IEEE Communications Magazine, vol. 51, no. 1, pp. 42–49, January 2013.
T. B. Brown, “Language models are few-shot learners,” in Proc. Adv. Neural Inf. Process. Syst. (NeurIPS), vol. 33, 2022, pp. 1877–1901.
Y. Chen and P. Esmaeilzadeh, “Generative AI in medical practice: In-depth exploration of privacy and security challenges,” J. Med. Internet Res., vol. 26, Mar. 2024, Art. no. e53008, doi: 10.2196/53008.
M. Dahl, V. Magesh, M. Suzgun, and D. E. Ho, “Large legal fictions: Profiling legal hallucinations in large language models,” J. Legal Anal., vol. 16, no. 1, pp. 64–93, Jan. 2024.
V. De Nitto Personè and V. Grassi, “Architectural Issues for Self-Adaptive Service Migration Management in Mobile Edge Computing Scenarios,” 2019 IEEE International Conference on Edge Computing (EDGE), Milan, Italy, 2019, pp. 27–29.
W. Fan, Y. Ding, L. Ning, S. Wang, H. Li, D. Yin, T.-S. Chua, and Q. Li, “A survey on RAG meeting LLMs: Towards retrieval-augmented large language models,” in Proc. 30th ACM SIGKDD Conf. Knowl. Discovery Data Mining, Aug. 2024, pp. 6491–6501.
Y. Gao, Y. Xiong, X. Gao, K. Jia, J. Pan, Y. Bi, Y. Dai, J. Sun, M. Wang, and H. Wang, “Retrieval-augmented generation for large language models: A survey,” 2023, arXiv:2312.10997.
V. C. Hu, D. F. Ferraiolo, and D. R. Kuhn, Guide to Attribute Based Access Control (ABAC) Definition and Considerations, document NIST Special Publication 800-162, Jan. 2014.
European Parliament, General Data Protection Regulation (GDPR), document Regulation (EU) 2016/679, Apr. 2016. [Online]. Available: https://gdpr-info.eu/
G. -Q. Yan and M. Shao-jie, “Cloud-Edge-End Simulation System Architecture Study,” 2020 2nd International Conference on Advances in Computer Technology, Information Science and Communications (CTISC), Suzhou, China, 2020, pp. 95–99.
D. Hawking and N. Craswell. Very large-scale retrieval and web search. In TREC: Experiment and Evaluation in Information Retrieval, E. Voorhees and D. Harman, eds, MIT Press, 2005.
P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W.-t. Yih, T. Rocktäschel, S. Riedel, and D. Kiela, “Retrieval-augmented generation for knowledge-intensive NLP tasks,” in Proc. Adv. Neural Inf. Process. Syst. (NeurIPS), vol. 33, 2020, pp. 9459–9474.
F. Li, B. Luo and P. Liu, “Secure Information Aggregation for Smart Grids Using Homomorphic Encryption,” 2010 First IEEE International Conference on Smart Grid Communications, Gaithersburg, MD, USA, 2010, pp. 327–332.
Y. Li, Q. Cheng, X. Liu and X. Li, “A Secure Anonymous Identity-Based Scheme in New Authentication Architecture for Mobile Edge Computing,” in IEEE Systems Journal, vol. 15, no. 1, pp. 935–946, March 2021.
X. Li, Z. Li, S. Chen, Y. Xu, Q. Du, M. Tan, J. Huang, and W. Lin, “AlphaFin: Benchmarking financial analysis with retrieval-augmented stock-chain framework,” in Proc. LREC-COLING, May 2024, pp. 773–783.
R. Laheri, "AI-Enhanced Biometric Systems for Insurance: Secure Authentication and Regulatory Compliance," 2025 2nd International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF), Chennai, India, 2025, pp. 1-6, doi: 10.1109/ICECONF65644.2025.11379513.
Mishra, D., Dharminder, D., Yadav, P., Rao, Y. S., “A provably secure dynamic ID-based authenticated key agreement framework for mobile edge computing without a trusted party.” J. Inf. Secur. Appl., 55, 102648, 2020.
S. Rose, O. Borchert, S. Mitchell, and S. Connelly, “Zero trust architecture,” Nat. Inst. Stand. Technol., Gaithersburg, MD, USA, Tech. Rep. SP 800-207, Aug. 2020.
R. Sandhu, E. Coyne, H. Feinstein, and C. Youman, “Role-based access control models,” IEEE Comput., vol. 29, no. 2, pp. 38–47, Feb. 1996.
M. Tanveer, A. U. Khan, N. Kumar, A. Naushad and S. A. Chaudhry, “A Robust Access Control Protocol for the Smart Grid Systems,” in IEEE Internet of Things Journal, vol. 9, no. 9, pp. 6855–6865, 1 May1, 2022.
U.S. Department of Health & Human Services. ( 1996 ). Health Insurance Portability and Accountability Act of 1996 (HIPAA). [Online]. Available: https://www.hhs.gov/hipaa/
C. P. Vineetha and C. A. Babu, “Smart grid challenges, issues and solutions ”, 2014 International Conference on Intelligent Green Building and Smart Grid (IGBSG), Taipei, Taiwan, 2014, pp. 1–4.
M. Wazid, P. Bagga, A. K. Das, S. Shetty, J. J. P. C. Rodrigues and Y. Park, “AKM-IoV: Authenticated Key Management Protocol in Fog Computing-Based Internet of Vehicles Deployment,” in IEEE Internet of Things Journal, vol. 6, no. 5, pp. 8804–8817, Oct. 2019.
E. G. Woo, M. C. Burkhart, E. Alsentzer, and B. K. Beaulieu-Jones, “Synthetic data distillation enables the extraction of clinical information at scale,” npj Digit. Med., vol. 8, no. 1, pp. 1–13, May 2025.
X. Yang, X. Guan, N. Wang, Y. Liu, H. Wu and Y. Zhang, “Cloud-Edge-End Intelligence for Fault-Tolerant Renewable Energy Accommodation in Smart Grid,” in IEEE Transactions on Cloud Computing, vol. 11, no. 2, pp. 1144–1156, 1 April-June 2023.
Y. Wei, “Data organization patterns for cloud enterprise applications,” in Proc. Asia–Pacific Services Comput. Conf. (APSCC), 2014, pp. 1–7.

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