Blockchain and Mobile Payment: Assessment on Privacy and Usability and a Scheme for Enhancement
Bibliographic record
Abstract
Blockchain is a new paradigm to realize payment without a single Trusted-Third-Party. The technology exploits cryptography to secure all the transactions that are available to participating nodes for validation via distributed consensus algorithms. Blockchain-based architectures for mobile payment face challenges like transaction privacy and performance issues. We analyze these architectures and assess the privacy issues and the performance in the real world. Then, we propose a payment scheme to guarantee the privacy of the transactions with discussions on tricks to improve the performance. We study the feasibility of implementation of the proposed scheme, both on public and consortium Blockchains. The payment scheme ensures that a participating node has access to only a part of the meaningful data of a transaction. The expected performance with Hyperledger Blockchain (with less than 16 participating nodes) is more than 1000 TPS. We indicate updates to decrease the duration of a transaction from 15 S to less than 1 S with public Ethereum Blockchain.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".