AT-CBDC: Achieving Anonymity and Traceability in Central Bank Digital Currency
Bibliographic record
Abstract
In this paper, we propose a new central bank digital currency (CBDC) system based on the two-tier architecture. The proposed system enhances the traditional bank-user framework of electronic cash and employs the commercial banks for account and coin management. The coin splitting is supported during coin withdrawal of users and the coin combination is achieved for coin deposit at the commercial banks, such that the efficiency of coin management is improved. The other distinguished feature is that the proposed system achieves the anonymity against the commercial banks, while enabling the central bank to support user tracing and double-spending prevention. Specifically, by utilizing the BBS+ signatures, the users can create bank accounts, withdraw coins, and deposit the received coins at the commercial banks without exposing their real identities. As a trusted party, the central bank is responsible for money issuing and financial regulation. In addition, to ensure the system inclusive, users can receive payments from others even they do not have bank accounts at commercial banks. Finally, we demonstrate that the proposed system achieves the desirable properties of balance, anonymity, and traceability and show the efficiency and practicality for the implementation on mobile devices.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".