THE ROLE OF BLOCKCHAIN TECHNOLOGY IN DIGITAL IDENTITY MANAGEMENT: CHALLENGES, OPPORTUNITIES, AND FUTURE DIRECTIONS
Main Article Content
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
Article Details
Section

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain the copyright of their manuscripts, and all Open Access articles are disseminated under the terms of the Creative Commons Attribution License 4.0 (CC-BY), which licenses unrestricted use, distribution, and reproduction in any medium, provided that the original work is appropriately cited. The use of general descriptive names, trade names, trademarks, and so forth in this publication, even if not specifically identified, does not imply that these names are not protected by the relevant laws and regulations.
How to Cite
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.