A Privacy-Preserving and Secure Scheme for Online Payment in the Realm of Mobile Commerce
Bibliographic record
Abstract
Smart mobile devices play an undeniable role in our daily lives. Following the current trend, mobile commerce has gained much attention in the recent decade. In this paper, we design a novel privacy-preserving scheme for online payment in the context of mobile commerce to resolve existing security and privacy challenges. In addition to concerns about security and privacy, the regulatory control imposed by governments is another crucial factor to take into account. In other words, they must be able to trace suspicious financial transactions through regulatory agencies. We use a partially blind signature and blockchain technology to provide strong security and privacy protection. In our proposed protocol, a payer pays for online services and digital goods via a token, which is valued and signed by the bank. Furthermore, the payer can obtain the remaining balance of the price, in comparison to the service cost, by utilizing another token. Our protocol ensures anonymity and untraceability for daily purchases in which a huge volume of money is not moved. Hence, it can comply with regulatory agency guidelines. Comprehensive security and privacy analyses demonstrate our proposed scheme preserves privacy and withstands attacks in this area. According to performance analysis, our proposed protocol is also efficient, lightweight, and feasible.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".