A Hyperledger-Based Secure Framework for Academic Certificate Authentication Using Blockchain
Bibliographic record
Abstract
Certificate authentication is often a tedious and complex process, particularly for critical documents such as academic degrees, which require rigorous verification to prevent fraud.Traditional methods are slow, prone to errors, and heavily reliant on manual effort, making it difficult to detect sophisticated counterfeit certificates that can undermine the credibility of both students and issuing institutions.To address these challenges, this study proposes and develops a blockchain-based Certification Verification System using Hyperledger Fabric.The system enables universities to issue and upload academic credentials to a secure, permissioned blockchain, ensuring the immutability, authenticity, and confidentiality of records.By decentralizing certificate storage, the platform allows only authorized parties to access and verify data, reducing processing time, enhancing transparency, and safeguarding against tampering.Hyperledger Fabric was selected for its privacy features, scalability, and enterprise-grade capabilities, eliminating the need for cryptocurrency while providing controlled access.Each participant is equipped with device-specific certificates for robust authentication, further reinforcing security.In this study, evaluation of Blockchain Platforms Based on TPS, Consensus, and Certificate Suitability using Hyperledger Fabrics outperform in the comparison with Ethherium and botcoin platforms.This integrated system empowers students to manage and share their verified credentials easily and allows employers to perform real-time, trustworthy verification.Overall, the study demonstrates that blockchain technology, when implemented through permissioned frameworks like Hyperledger Fabric, offers a reliable, future-ready solution for modernizing academic credential verification while upholding the principles of security, decentralization, and trust.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".