FEIPS: A Secure Fair-Exchange Payment System for Internet Transactions
Bibliographic record
Abstract
To be considered secure, a payment system needs to address a number of security issues. Besides fundamental security requirements, like confidentiality, data integrity, authentication and non-repudiation, another important requirement for a secure payment system is fair exchange. Many existing payment protocols require that customers must pay for products before their delivery (in the case of delivery of digital goods) or the delivery of the receipt (in the case of delivery of physical goods). This unfair situation should be eliminated afterward; that is, it is necessary to rebalance fairness for customers. To address these issues, we propose the Fair Exchange Internet Payment Protocol (FEIPS). The FEIPS protocol is designed for the payment of physical goods and falls into the category that uses a trusted third party for ensuring fair exchange. Although FEIPS has a strong emphasis on fair exchange, it still guarantees strong security properties, including confidentiality, data integrity, authentication and non-repudiation. The FEIPS protocol is designed to be simple and practical, unlike other similar protocols designed for the payment of physical goods. To demonstrate that FEIPS satisfies the desired properties, we perform a formal verification using the HLPSL language and the AVISPA tool.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".