Blockchain-Based, Privacy-Preserving, First Price Sealed Bid Auction (FPSBA) Verifiable by Participants
Bibliographic record
Abstract
The transparent, decentralized, and immutable properties of public blockchains make them appealing for applications that could benefit from these properties, including e-auctions. However, the usage of blockchains for such applications is hindered by concerns related to privacy and performance. We introduce a hybrid solution for e-auctions, leveraging the advantages of both centralized processing and the immutable registration offered by a public blockchain. In this approach, bids are processed by the auctioneer, while the masked bids are recorded on the blockchain. Consequently, the identity of the winner and their bid value is publicly accessible, while the remaining bid values remain confidential, known only to the bidder and the auctioneer and verifiable against the public results of the auction. These bids remain sealed unless the bidder chooses to disclose them during a dispute event. In this paper, we explain the proposed solution and present empirical analysis of its performance drawing on the outcomes of our preliminary experiments. We elaborate achieving promised privacy while showing 50% improvement over blockchain-based verifiable sealed-bid auction involving fewer than 15 bidders.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".