Efficiently Achieving Full Three-Way Non-repudiation in Consumer-Level eCommerce and M-Commerce Transactions
Bibliographic record
Abstract
eCommerce has rapidly turned into a trillion dollar a year industry. Now an integral part of modern economies, it is continuing to expand, especially in the form of M- commerce. Numerous solutions have been proposed to secure consumer-level eCommerce and M-commerce transactions. The recent shift toward chip-and-PIN cards in some jurisdictions, and similar technologies that require pre-transaction customer authorization, has begun to shift the legal liability for security breaches from the financial institutions onto the customers themselves. Because it is relatively easy to acquire someone's PIN (e.g., through shoulder surfing, cameras placed in the environment, touch sensitive overlays, or compromised debit or credit card terminals), a core issue is that customers are given no formal means by which they can prove their involvement (or lack thereof) in a given transaction. To make matters worse, the supposition becomes that they were careless with their PIN and, hence, by the card holder agreement, hold financial responsibility for the transaction(s). This work addresses said problem by developing a secure and efficient (<; 5 second) consumer-level eCommerce/M-Commerce transaction protocol that supports non-repudiation for the customer, merchant, and financial institution. Hence, post-transaction, each participant holds sufficient information to prove what the others did (or did not) do. To our knowledge this is the first transaction protocol to support such full 3-way non-repudiation.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".