Toward Secure and Transparent Global Authentication: A Blockchain-Based System Integrating Biometrics and Subscriber Identification Module
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it