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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM50313
Volume:
9 |
6 |
Jun 2025
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
In todayâs hyper-digital landscape, secure and private digital identity management is paramount. With increasing reliance on online platforms for services, transactions, and communications, individuals require robust systems that offer privacy, control, and trust. Traditional centralized and federated identity systemsâlike those offered by major tech companiesâhave become inadequate, exposing users to significant risks such as data breaches, identity theft, and surveillance. These systems fail to provide users with autonomy over their personal information, as identities are controlled and stored by third-party providers. To overcome these limitations, this project proposes an advanced self-sovereign digital identity (SSI) framework that integrates decentralized blockchain architecture with machine learning- driven trust evaluation. By leveraging blockchainâs immutable and tamper-resistant ledger, digital identities are stored securely and directly under user control. A Unique Personal Identifier (UPI) is used to authenticate users, eliminating the need for centralized identity providers. To further enhance security, a Logistic Regression model is utilized to assess the trustworthiness of service providers. In scenarios where trust is not established, the system generates masked credentials using a Lookup Substitution Algorithm, ensuring that user privacy is preserved during verification processes. This blockchain and AI-powered approach provides a user-centric, transparent, and secure alternative to traditional identity systemsâempowering individuals to take ownership of their digital identities while mitigating the risks associated with data misuse and centralized control.
KEYWORDS
Publication Date
6/12/2025
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
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