Analyzing Issues of Privacy and Offline Transactions In Central Bank Digital Currencies
Bibliographic record
Abstract
With the popularity of cryptocurrencies like bitcoin and Ethereum, many central banks have begun to look into issuing their own digital currency. For many central banks, the goal of a central bank digital currency (CBDC) is to provide a user experience similar to paper money, but fully digital. The central bank also plays an important role in the system, namely acting as a source of trust. This source of trust is an important differentiator, as it incentivizes the use of alternative technologies to confirm transactions, rather than using inefficient consensus protocols such as a proof-of-work blockchain. \nIn order to act as a true paper money alternative, two of the biggest hurdles that need to be overcome are privacy and offline transactions. In this thesis, we will examine these issues in more detail, discussing what problems they pose and what (if any) solutions have been presented in the existing literature. Additionally, we will be offering our own solutions, using hash chains to provide user privacy, and presenting a prototype CBDC system for offline transactions.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".