RBSmix: A Regulatable Privacy‐Preserving Method for Cryptocurrency
Bibliographic record
Abstract
Public chains represented by Bitcoin and Ethereum do not require users to use their real names, and transaction data are open to the whole network. Analysed based on this, researchers have achieved the deanonymization of blockchain transactions to a certain extent. Based on the existing blockchain transaction privacy protection scheme, the true link relationship between the transaction sender and receiver is hidden, which brings difficulties to regulation. In this paper, we propose a cryptocurrency mixing service RBSmix, which allows users to reestablish their financial privacy in Bitcoin and related cryptocurrencies. RBSmix, through blind signature to prevent attackers from linking input and output addresses, by the threshold secret sharing algorithm, encryption technology, and a regulation team, combined with the idea of voting, tracks the source of funds for illegal addresses. Experiments show that the scheme scales to large numbers of users and can provide users with better privacy protection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".