Verifiable Sealed-Bid Auction on the Ethereum Blockchain.
Bibliographic record
Abstract
The success of the Ethereum blockchain as a decentralized application platform with a distributed consensus protocol has made many organizations start to invest into running their business on top of it. Technically, the most impressive feature behind the success of Ethereum is its support for a Turing complete language. On the other hand, the inherent transparency and, consequently, the lack of privacy poses a great challenge for many financial applications. In this paper, we tackle this challenge and present a smart contract for a verifiable sealed-bid auction on the Ethereum blockchain. In a nutshell, initially, the bidders submit homomorphic commitments to their sealed-bids on the contract. Subsequently, they reveal their commitments secretly to the auctioneer via a public key encryption scheme. Then, according to the auction rules, the auctioneer determines and claims the winner of the auction. Finally, we utilize interactive zero-knowledge proof protocols between the smart contract and the auctioneer to verify the correctness of such a claim. The underlying protocol of the proposed smart contract is partially privacy-preserving. To be precise, no information about the losing bids is leaked to the bidders. We provide an analysis of the proposed protocol and the smart contract design, in addition to the estimated gas costs associated with the different transactions.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".