Distributed architectures for electronic cash schemes: a survey1
Bibliographic record
Abstract
The volume of E-commerce transactions has considerably increased in the last several years. One of the most important aspects of such progress is the efforts made to develop and deploy dependable and secure payment infrastructures. Among these infrastructures is electronic cash, which is an attempt to reproduce the characteristics of paper cash in online transactions. Electronic cash schemes have so far been the purpose of a significant amount of research work. Although real-life deployments of such schemes are expected to take place in highly distributed environments, limited attention has been paid in the literature on underlying architectural issues. So far the focus has mostly been on addressing only security issues. However, for real-life deployment, distributed processing criteria such as performance, scalability and availability are of prime importance. In this paper, through a survey of the literature, we identify and analyse the different distributed architectural styles underlying existing e-cash schemes. We discuss the strengths and limitations of these architectures with respect to fundamental system distribution criteria. In light of such discussion, we make some recommendations for designing effective distributed e-cash systems from an architectural perspective.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".