Toward Secure and Transparent Global Authentication: A Blockchain-Based System Integrating Biometrics and Subscriber Identification Module
Bibliographic record
Abstract
The growing reliance on e-government services necessitates robust and secure user authentication. Existing solutions often suffer from limitations such as lack of transparency, compromise of user privacy, and reliance on a central server, thus introducing a single point of failure (SPOF). This paper proposes B2-GAS, a novel Biometric and Blockchain-based Global Authentication System, that addresses these shortcomings. B2-GAS leverages user biometrics on smartphones for strong identification and isolates sensitive cryptographic operations within a secure enclave on a SIM card. This approach safeguards user privacy and data security. By employing blockchain technology, B2-GAS eliminates SPOFs, ensures tamper-proof transaction storage, and guarantees transparency. Unlike existing protocols, which often rely on theoretical analysis, B2-GAS utilizes an emulated environment to assess its performance under realistic conditions. This allows for a more practical evaluation compared to purely theoretical approaches. B2-GAS exerts multiple factors during authentication including biometrics, a password, and a secret parameter to further enhance security. Rigorous security proofs demonstrate B2-GAS’s resistance to user impersonation, offline password-guessing, replay attacks, and brute-force attempts. Evaluation using the emulated environment and blockchain simulations demonstrates B2-GAS security parameters, performance, and computational overheads. By combining biometrics, secure SIM enclaves, and blockchain, B2-GAS offers a unique and robust authentication solution for diverse e-government services in smart cities.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".