Advancing Scholarship Management: A Blockchain-Enhanced Platform With Privacy-Secure Identities and AI-Driven Recommendations

被引:0
作者
Nguyen-Hoang, Tu-Anh [1 ,2 ]
Hoang, Ngoc Cu [1 ,2 ]
Hua, Phu Thien [1 ,2 ]
Thi, Mong-Thy Nguyen [1 ,2 ]
Ta, Thu-Thuy [1 ,2 ]
Nguyen, Thu [1 ,2 ]
Tan-Vo, Khoa [1 ,2 ]
Dinh, Ngoc-Thanh [3 ]
Nguyen, Hong-Tri [4 ]
机构
[1] Univ Informat Technol, Fac Informat Sci & Engn, Ho Chi Minh City 720400, Vietnam
[2] Vietnam Natl Univ, Ho Chi Minh City 700000, Vietnam
[3] Ind Univ Ho Chi Minh City, Ho Chi Minh City 700000, Vietnam
[4] Aalto Univ, Sch Sci, Dept Comp Sci, Espoo 02150, Finland
来源
IEEE ACCESS | 2024年 / 12卷
关键词
Scholarships; Blockchains; Education; Smart contracts; Scalability; Qualifications; Costs; Transforms; Reviews; Prediction algorithms; Scholarship management; blockchain; scalability; zero-knowledge; zk-rollup; machine learning; SMART CONTRACTS;
D O I
10.1109/ACCESS.2024.3486078
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:168060 / 168090
页数:31
相关论文
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