A Private and Efficient Triple-Entry Accounting Protocol on Bitcoin
Bibliographic record
Abstract
The ‘Big Four’ accountancy firms dominate the auditing market, auditing almost all the Financial Times Stock Exchange (FTSE) 100 companies. This leads to people having to accept auditing results even if they may be poor quality and/or for inadequate purposes. In addition, accountants may provide different auditing results with the same financial data. These issues are hard for regulators such as the Financial Reporting Council to identify because of insufficient resources or inconsistent compliance. In this paper, we proposed a triple-entry accounting protocol to allow users to report Bitcoin transactions to a third-party auditor to comply with regulations such as the travel rule. It allows the auditor to easily detect anomalies and identify the non-compliant parties, whilst the blockchain itself provides a transparent and immutable record of these anomalies. Despite building on a public ledger, our solution preserves privacy and offers an interoperability layer for information exchange. Merkle proofs were used to record non-compliant transactions whilst allowing compliant transactions to be pruned from an auditor’s active database.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".