Advancing Scholarship Management : A blockchain-Enhanced Platform with Privacy-Secure Identities and AI-Driven Recommendations

Tu Anh Nguyen-Hoang*, Ngoc Cu Hoang, Phu Thien Hua, Mong Thy Nguyen Thi, Thu Thuy Ta, Thu Nguyen, Khoa Tan-Vo, Ngoc Thanh Dinh, Hong Tri Nguyen*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

Traditional scholarship management systems are often marred by inefficiencies, a lack of transparency, and significant concerns regarding data privacy. These challenges hinder the equitable distribution of educational funds and obscure the scholarship allocation process. This study aims to address these issues by proposing a blockchain-based scholarship management platform. However, scalability issues are introduced along with blockchain-based solutions, for which zk-rollup offers a promising solution. The integration of Self-Sovereign Identity Zero-Knowledge Proof assures secure and private submission of scholarship applications, safeguarding student data while maintaining transparency in the verification process. Additionally, a machine learning model is employed to predict scholarship qualification, functioning as a recommendation system that identifies and prioritizes deserving students. This data-driven approach proactively eliminates barriers that potentially impede deserving students from accessing financial aid, such as administrative oversights or a lack of self-assurance in their qualifications. Our experimental findings confirm the effectiveness of zk-rollups in enhancing transaction efficiency, demonstrating a reduction in transaction confirmation time by approximately 63.6% and a decrease in transaction costs by nearly 90%. Besides, the machine learning model achieved a good performance rating, achieving a balanced accuracy of 86.75% and a mean average precision of 91.68% on a realistically imbalanced test set, reflecting real-world conditions.

Original languageEnglish
Article number3486078
Pages (from-to)168060-168090
Number of pages31
JournalIEEE Access
Volume12
Early online date2024
DOIs
Publication statusPublished - 2024
MoE publication typeA1 Journal article-refereed

Keywords

  • blockchain
  • machine learning
  • scalability
  • Scholarship management
  • zero-knowledge
  • zk-rollup

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