IEEE Access · 2024 · 12 citations · 37 references
ComputingComputer ScienceHigher EducationAuthenticationCryptographyPrivacy EngineeringDigital IdentityDigital Identity VerificationIdentity ManagementPrivacy In EducationScholarship AllocationStudent PrivacyPrivacy-preserving LearningScholarship Budget AllocationStudent Data CollectionStudent Data SecurityStudent Data SharingZero-knowledge Proof ProtocolsZero-knowledge Proof SystemsZero-knowledge Rollups
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.
37
SMOTE: Synthetic Minority Over-sampling Technique
Nitesh V. Chawla, Kevin W. Bowyer, Lawrence Hall et al. · Journal of Artificial Intelligence Research · 2002 · 31.2K citations · Full text
Formalizing and Securing Relationships on Public Networks
Nick Szabo · First Monday · 1997 · 2.1K citations · Full text
Blockchain technology overview
Dylan Yaga, Peter Mell, Nik Roby et al. · 2018 · 1.5K citations · Full text
Michael Nofer, Peter Gomber, Oliver Hinz et al. · Business & Information Systems Engineering · 2017 · 1.2K citations