A COMPREHENSIVE REVIEW OF BLOCKCHAIN-ENABLED MULTIMODAL BIOMETRIC AUTHENTICATION FOR PRIVACY-PRESERVING ACCESS CONTROL IN NEXT-GENERATION E-HEALTH SYSTEMS

Authors

DOI:

https://doi.org/10.33003/fjs-2026-1004-4650

Keywords:

Access-control, Authentication, E-health system, Biometrics, Blockchain, Privacy-Preserving, Security

Abstract

The next-generation e-health systems, which include electronic health records (EHRs), telemedicine platforms, and Internet of Medical Things (IoMT) environments, need a strong access control system that protects sensitive medical data while maintaining user privacy. The conventional access control systems face security risks because of credential theft, spoofing attacks, and their reliance on centralized trust, and their inability to scale. Blockchain-enabled multimodal biometric authentication provides a secure and decentralized solution for access control in e-health systems, according to current technological advancements. This paper provides an extensive assessment of blockchain-based multimodal biometric authentication systems, which deliver privacy-protecting access control solutions for future e-health systems. The review further examines central techniques for protecting biometric templates, zero-knowledge proofs, homomorphic encryption, and secure off-chain storage systems. The research assessed existing methods by comparing efficiency for access control, ability to protect user data, capacity to handle growing user needs, ability to work with other systems, and compliance with the General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA) regulations. The research identifies open challenges that need resolution, which include biometric data revocability, latency constraints, cross-platform interoperability, and limited real-world deployments. The study presents upcoming research paths that will investigate lightweight blockchain systems, post-quantum cryptography, cross-chain medical identity management, and adaptive access control systems in extensive e-health environments. The review demonstrates that blockchain-based multimodal biometric authentication serves as a suitable foundation that enables secure access control through decentralized systems that protect user privacy in upcoming e-health technologies.

Author Biography

  • Afolayan . A. Obiniyi, Federal University Lokoja

    H.O.D, Computer Science Department.

References

Abbas, S. R., Abbas, Z., Rehman, M. U., & Lee, S. W. (2026).Blockchain for smart healthcare: A systematic review of security, interoperability, and AI–IoT integration.Digital Health. Advance online publication. https://doi.org/10.1177/20552076261420

Abdullahi, S. M., Sun, S., Wang, B., Wei, N., & Wang, H. (2024). Biometric template attacks and recent protection mechanisms: A survey. Information Fusion, 103, 102144. https://doi.org/10.1016/j.inffus.2023.102144

Alawiye, T. (2024). The impact of digital technology on healthcare delivery and patient outcomes.E-Health Telecommunication Systems and Networks, 13, 13–22. https://doi.org/10.4236/etsn.2024.132002

Alrawili, R., AlQahtani, A. A. S., & Khan, M. K. (2024). Comprehensive survey: Biometric user authentication application, evaluation, and discussion. Computers and Electrical Engineering, 119(Part A), 109485. https://doi.org/10.1016/j.compeleceng.2024.109485

Anabor, J. O. (2022). Multimodal biometrics: For authorisation and authentication. In K. Daimi, G. Francia III, & L. H. Encinas (Eds.), Breakthroughs in digital biometrics and forensics (pp. [insert page range if known]). Springer. https://doi.org/10.1007/978-3-031-10706-1_2

Andrew, J., Isravel, D. P., Sagayam, K. M., Bhushan, B., Sei, Y., & Eunice, J. (2023). Blockchain for healthcare systems: Architecture, security challenges, trends and future directions. Journal of Network and Computer Applications, 215, 103633. https://doi.org/10.1016/j.jnca.2023.103633

Babu, A., Balasubramanian, K. R., Singh, A., Sureshbabu, M. R., & Natarajan, Y. (2025). Decentralized digital identity: A blockchain and neural network approach.Premier Journal of Science. https://doi.org/10.70389/PJS.100142

Bala, N., Gupta, R., & Kumar, A. (2022). Multimodal biometric system based on fusion techniques: A review. Information Security Journal: A Global Perspective, 31(3), 289–337. https://doi.org/10.1080/19393555.2021.1974130

Bhairnallykar, S. T., &Narawade, V. (2024). A comprehensive exploration of convolutional neural network architectures in deep learning. In Z. Illés, C. Verma, P. J. S. Gonçalves, & P. K. Singh (Eds.), Proceedings of International Conference on Recent Innovations in Computing (ICRIC 2023) (Lecture Notes in Electrical Engineering, Vol. 1195, pp. [insert page range if known]). Springer. https://doi.org/10.1007/978-981-97-3442-9_12

Bhattacharya, S., Seth, D. K., Panyam, S., &Gangrade, P. (2024). Enhancing digital privacy: The application of zero-knowledge proofs in authentication systems. International Journal of Computer Trends and Technology, 72(4), 34–41. https://doi.org/10.14445/22312803/IJCTT-V72I4P104

Gao, Z., & Yan, W. (2025). The real-time data processing framework for blockchain and edge computing. Alexandria Engineering Journal, 120, 50–61. https://doi.org/10.1016/j.aej.2025.01.092

Ghafourian, M., Sumer, B., Vera-Rodriguez, R., Fierrez, J., Tolosana, R., & Morales, A. (2023).Combining blockchain and biometrics: A survey on technical aspects and a first legal analysis.arXiv. https://doi.org/10.48550/arXiv.2302.10883

Javaid, M., Haleem, A., Singh, R. P., & Suman, R. (2023).Towards insighting cybersecurity for healthcare domains: A comprehensive review of recent practices and trends.Cyber Security and Applications, 1, Article 100016. https://doi.org/10.1016/j.csa.2023.100016

Jiao, T., Guo, C., Feng, X., Chen, Y., & Song, J. (2024). A comprehensive survey on deep learning multi-modal fusion: Methods, technologies and applications. Computers, Materials & Continua, 80(1), 1–35. https://doi.org/10.32604/cmc.2024.053204

Khan, T., Tian, W., Ilager, S., &Buyya, R. (2022). Workload forecasting and energy state estimation in cloud data centres: ML-centric approach. Future Generation Computer Systems, 128, 320–332. https://doi.org/10.1016/j.future.2021.10.019

Kozierkiewicz, A., Graczyk, A., &Pimenidis, E. (2025). A multimodal approach to biometric authentication. In H. Jahankhani& B. Issac (Eds.), Cybersecurity and human capabilities through symbiotic artificial intelligence (ICGS3 2023) (pp. 481–496). Springer. https://doi.org/10.1007/978-3-031-82031-1_24

Li, S., Nguyen, H.-T., & Cheah, C. C. (2023). A theoretical framework for end-to-end learning of deep neural networks with applications to robotics. IEEE Access, 11, 21992–22006. https://doi.org/10.1109/ACCESS.2023.3249280

Liu, J., Liu, C., Lin, M., & Xu, G. (2025). Comprehensive survey of blockchain consensus mechanisms: Analysis, applications, and future trends. Computer Networks, 272, 111661. https://doi.org/10.1016/j.comnet.2025.111661

Mao, X., Chen, Y., Deng, C., & Zhou, X. (2023). A novel privacy-preserving biometric authentication scheme. PLoS ONE, 18(5), e0286215. https://doi.org/10.1371/journal.pone.0286215

Muhammad, A. (2025, July).Multimodal biometric authentication: Integrating fingerprints, face, and voice using AI: An AI-based approach to secure identity verification using fingerprint, face, and voice biometrics (Preprint). https://doi.org/10.31224/4808

Rathgeb, C., Merkle, J., Scholz, J., Tams, B., &Nesterowicz, V. (2022). Deep face fuzzy vault: Implementation and performance. Computers & Security, 113, 102539. https://doi.org/10.1016/j.cose.2021.102539

Shaheed, K., Mao, A., Qureshi, I., & others. (2021). A systematic review on physiological-based biometric recognition systems: Current and future trends. Archives of Computational Methods in Engineering, 28, 4917–4960. https://doi.org/10.1007/s11831-021-09560-3

Shaikh, M., Wiil, U. K., & Ebrahimi, A. (2026). An overview and comparison of blockchain consensus mechanisms. International Journal of Networked and Distributed Computing, 14, 4. https://doi.org/10.1007/s44227-025-00087-8

Sharma, S., & Dwivedi, R. (2024). A survey on blockchain deployment for biometric systems. IET Blockchain, 4(2), 124–151. https://doi.org/10.1049/blc2.12063

Suleski, T., Ahmed, M., Yang, W., & Wang, E. (2023). A review of multi-factor authentication in the Internet of Healthcare Things. DIGITAL HEALTH, 9, 20552076231177144. https://doi.org/10.1177/20552076231177144

Tawfik, A. M., Al-Ahwal, A., Tag Eldien, A. S., & Zayed, H. H. (2025). Blockchain-based access control and privacy preservation in healthcare: A comprehensive survey. Cluster Computing, 28, 529. https://doi.org/10.1007/s10586-025-05308-x

Tripathi, G., Ahad, M. A., & Casalino, G. (2023).A comprehensive review of blockchain technology: Underlying principles and historical background with future challenges.Decision Analytics Journal, 9, Article 100344. https://doi.org/10.1016/j.dajour.2023.100344

Tripathi, G., Ahad, M. A., & Casalino, G. (2023). A comprehensive review of blockchain technology: Underlying principles and historical background with future challenges. Decision Analytics Journal, 9, 100344. https://doi.org/10.1016/j.dajour.2023.100344

Ullah, F., He, J., Zhu, N., Wajahat, A., Nazir, A., Qureshi, S., Pathan, M. S., & Dev, S. (2024). Blockchain-enabled EHR access auditing: Enhancing healthcare data security. Heliyon, 10(16), e34407. https://doi.org/10.1016/j.heliyon.2024.e34407

Wang, X., Yan, Z., Zhang, R., & Zhang, P. (2021).Attacks and defenses in user authentication systems: A survey.Journal of Network and Computer Applications, 188, Article 103080. https://doi.org/10.1016/j.jnca.2021.103080

Yang, W., Wang, S., Cui, H., Tang, Z., & Li, Y. (2023). A review of homomorphic encryption for privacy-preserving biometrics. Sensors, 23(7), 3566. https://doi.org/10.3390/s23073566

Zaidi, T., & Mallik, S. (2025). Software applications for biometric informatics. In S. L. Tripathi, V. E. Balas, M. Mahmud, & S. Banerjee (Eds.), Machine learning models and architectures for biomedical signal processing (pp. 475–486). Elsevier. https://doi.org/10.1016/B978-0-443-22158-3.00019-3

Zhou, L., Diro, A., Saini, A., Kaisar, S., & Hiep, P. C. (2024). Leveraging zero-knowledge proofs for blockchain-based identity sharing: A survey of advancements, challenges, and opportunities. Journal of Information Security and Applications, 80, 103678. https://doi.org/10.1016/j.jisa.2023.103678

Zouaghi, I., Barka, E., & Kerrache, C. A. (2022). Privacy-preserving storage for blockchain-based e-health systems. Future Generation Computer Systems, 128, 321–334. https://doi.org/10.1016/j.future.2021.10.019

Architecture of Blockchain-Enabled Multimodal Biometric Authentication in E-Health Systems (Chelladurai & Pandian, 2022)

Downloads

Published

23-02-2026

How to Cite

Babalola, O. S., & Obiniyi, A. . A. (2026). A COMPREHENSIVE REVIEW OF BLOCKCHAIN-ENABLED MULTIMODAL BIOMETRIC AUTHENTICATION FOR PRIVACY-PRESERVING ACCESS CONTROL IN NEXT-GENERATION E-HEALTH SYSTEMS. FUDMA JOURNAL OF SCIENCES, 10(4), 132-138. https://doi.org/10.33003/fjs-2026-1004-4650