Inducing Trust in Blockchain-enabled IoT Marketplaces Through Reputation and Dispute Resolution
Bibliographic record
Abstract
In order for the metaverse to achieve its full potential, access to data from the physical world is required. The Internet of Things is a potential data provider, but a widely adopted marketplace to enable data monetization and trade with the metaverse does not yet exist. One of the reasons is the level of trust between the participants regarding the integrity of the exchanged data. In this paper, a reputation system is proposed that aims to alleviate this problem by providing a mechanism to ensure the validity of the traded data. Under this system, every seller is assigned a score, which buyers can use later to make informed decisions while ensuring it is economically disadvantageous for illicit actors to manipulate the scores. A dispute resolution scheme is also presented that acts as a fail-safe mechanism to further establish trust in this system, along with a proof-of-concept prototype of the proposed reputation system. Empirical results in this paper show the feasibility of the proposed system and provide insight in its behaviour with respect to its parameters.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".