Counter: A Novel Scheme Ensuring Compliance and Privacy in Cryptocurrency-Based Blockchain
Bibliographic record
Abstract
An extremely secure cryptocurrency, commonly known as the Decentralized Anonymous Payment System (DAP), stands as one of the most innovative and widely embraced applications in the field of blockchain. Although the DAP provides a strong degree of transaction privacy and user anonymity, it has been utilized illegally in criminal activities, constituting security challenges to governments and financial institutions. In this paper, we propose a solution known as Counter: A Novel Scheme Ensuring Compliance and Privacy in Cryptocurrency-Based Blockchain, which aims to preserve transaction privacy while providing governments with supervision and enforcing regulations over transactions. Our suggested counter imposed limitations on the transfer of large amounts of cryptocurrencies and also imposed a restriction on the trading of cryptocurrencies during specific time periods. The principal feature of our proposed system involves employing the Order-Preserving Encryption (OPE) and Pedersen Commitment to implement a financial policy ensuring that the transferred amount remains within the allowed range and does not exceed the limit. Besides, we utilized the Unix timestamp to guarantee that the transaction is carried out within the allowed date and time frame. We utilized the RSA accumulator to provide government supervision and enforce regulations. Our suggested counter employs the combination of the ring signature, Pedersen Commitment, and stealth address to protect transaction privacy. Our system fulfills all the security requirements for organizing digital assets that have been defined by international financial institutions and authorities. Finally, the assessment shows that our system is effective and efficient compared to other 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 | 0.002 | 0.005 |
| 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 | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".