Enabling Regulatory Compliance and Enforcement in Decentralized Anonymous Payment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Decentralized anonymous payment (DAP) enables users to directly transfer cryptocurrencies privately without passing through a central authority. Anonymous cryptocurrencies have been proposed to improve the privacy degree of DAP systems, such as Zerocash and Monero. However, the strong degree of privacy may cause new regulatory concerns, i.e., the anonymity of transactions can be used for illegal activities, such as money laundering. In this paper, we propose a novel DAP scheme that supports regulatory compliance and enforcement. We first introduce regulators into the system, who define regulatory policies for anonymous payment, and the policies are enforced through commitments and non-interactive zero-knowledge proofs for compostable statements. By doing so, users can prove that transactions are valid and comply with regulations. A tracing mechanism is embedded in the scheme to allow regulators to recover the real identities of users when suspicious transactions are detected. The formal security model and proof are provided to demonstrate that the proposed scheme can achieve desired security properties, and the performance evaluation shows its high efficiency.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 it