Bibliographic record
Abstract
Fair Exchange is a fundamental problem in the exchange of digital items with direct application to electronic commerce. In a fair exchange protocol, two parties want to exchange their corresponding items such that either both receive the other's item, or neither of them receives anything. It has been shown that fair exchange without a trusted third party (TTP) is not possible. Optimistic fair exchange protocols limit the role of TTP to the case that one of the parties misbehaves. OptiSwap (Eckey et al., 2020) is a fair exchange protocol for the exchange of confidential digital items with digital coins. OptiSwap uses a smart contract as the TTP and allows the buyer to use an interactive dispute resolution protocol with the seller (mediated through smart contract) to generate a proof of misbehaviour for a misbehaving seller. We show that OptiSwap's dispute resolution protocol leaks information about the item to the smart contract (public) which can completely reveal the item to the public, and this provides an opportunity for a malicious buyer to pose a credible threat to the fairness guarantee of the system. We propose and design privacy-enhanced OptiSwap that prevents the leakage of information and guarantees security and fairness of the exchange without significantly affecting the efficiency of the protocol. We prove security of the new protocol in an extension of the universal composability for non-monolithic adversaries, and implement and evaluate its efficiency against the original OptiSwap. We discuss our results and suggest directions for future research.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".