An Assessment Framework to Offline Functionality in Central Bank Digital Currencies
Bibliographic record
Abstract
The emergence of blockchain technology has reshaped the financial sector, leading to the introduction of new financial products and services. In parallel, central banks worldwide are investigating the possibility of issuing central bank digital currency (CBDC) and the potential use of blockchain as a foundational technology. Of particular interest is offline functionality, where transactions can be completed even if neither party is connected to the ledger at the time of transaction. Such offline functionality carries unique opportunities for countries and policy makers, but also comes with a new set of risks. Proposed herein is an assessment framework as a critical modeling tool towards risk evaluation. The framework is broadly applicable to blockchain-based solutions that support multiple off-chain transactions prior to synchronization. Urban and rural environments were modeled to demonstrate the model’s fidelity and flexibility. A test offline digital currency solution was evaluated via threat level experiments to ascertain the prototype’s resilience to varying levels of malicious activity. Results illustrate how various parameter configurations affect resilience to malicious activity and information propagation, demonstrating the effectiveness of the framework in providing valuable insights for the design of offline currency systems.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".