THE ROLE OF BLOCKCHAIN TECHNOLOGY IN DIGITAL IDENTITY MANAGEMENT: CHALLENGES, OPPORTUNITIES, AND FUTURE DIRECTIONS

Authors

  • Rasulov Hasan Rustamovich Asia International University, teacher of the "General Technical Sciences" department

DOI:

https://doi.org/10.55640/

Keywords:

Artificial Intelligence, cybersecurity, machine learning, deep learning, threat detection, intrusion detection systems, adversarial AI, neural networks, automated security, cyber defense.

Abstract

This paper explores the transformative potential of blockchain technology in digital identity management systems. As digital services expand globally, secure, privacy-preserving, and user-centric identity solutions have become critical. Traditional centralized identity systems are vulnerable to data breaches, identity theft, and unauthorized access. Blockchain-based identity frameworks offer decentralized architectures, cryptographic security, immutability, and enhanced user control over personal data. This study examines the integration of distributed ledger technology (DLT), smart contracts, zero-knowledge proofs, and decentralized identifiers (DIDs) into identity management ecosystems. It analyzes applications in financial services, e-government, healthcare, education, and cross-border authentication. The research also discusses key challenges including scalability, interoperability, regulatory compliance, privacy concerns, and governance models. Findings indicate that blockchain-enabled identity systems significantly reduce fraud risks, improve transparency, and empower users with self-sovereign identity control, although successful implementation requires technical standardization, legal clarity, and robust infrastructure development

Downloads

Download data is not yet available.

References

1.Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

2.Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.

3.Sommer, R., & Paxson, V. (2010). Outside the Closed World: On Using Machine Learning for Network Intrusion Detection. IEEE Symposium on Security and Privacy, 305-316.

4.Buczak, A. L., & Guven, E. (2016). A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection. IEEE Communications Surveys & Tutorials, 18(2), 1153-1176.

5.Apruzzese, G., et al. (2022). The Role of Machine Learning in Cybersecurity. Digital Threats: Research and Practice, 3(1), 1-32.

6.Papernot, N., et al. (2018). Deep Learning-Based Security Analytics: Opportunities and Challenges. Proceedings of the IEEE Security and Privacy Workshops, 127-137.

Downloads

Published

2026-02-15

How to Cite

THE ROLE OF BLOCKCHAIN TECHNOLOGY IN DIGITAL IDENTITY MANAGEMENT: CHALLENGES, OPPORTUNITIES, AND FUTURE DIRECTIONS. (2026). Journal of Multidisciplinary Sciences and Innovations, 5(02), 1193-1196. https://doi.org/10.55640/

Similar Articles

11-20 of 2498

You may also start an advanced similarity search for this article.