Trusted audit with untrusted auditors: A decentralized data integrity Crowdauditing approach based on blockchain
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
Edge computing emerges as an alternative to cloud computing in the scenarios where the end devices require lower latency and faster access speeds. Edge nodes are deployed at the proximity of the end devices to reduce response time. On the other hand, the edge nodes are usually owned by small organizations that have limited operations and maintenance capabilities. Data on the edge may be easily damaged, due to external attacks or internal hardware failures. Therefore, it is essential to verify data integrity in edge computing. However, edge environment requires a different trust model compared with other computing and storage paradigm. Besides, compared with cloud storage, edge storage is decentralized and storage service participants may pose greater internal and external threats. This paper proposes a blockchain-based intelligent crowdsourcing audit approach (Crowdauditing) to achieve on-chain and off-chain credibility of audit results. The model relies on an untrusted auditor committee from the crowd to audit data integrity and uses smart contracts as the core of the intelligent system to ensure the reliability of result submission, the accuracy of the result judgment, and reasonable punishments and rewards. Specifically, an unbiased selection algorithm is proposed to achieve fairness during the auditor committee construction. An innovative two-stage submission strategy is proposed to ensure that the auditor committee can reach a consensus on the off-chain audit results. An incentive mechanism is carefully designed to force auditors providing audit services honestly to maximize their own rewards. Moreover, we modeled that as a game of n players, which proves the reliability of the result. Finally, we implement a prototype of Crowdauditing based on smart contracts. The extensive experimental results demonstrate the effectiveness of Crowdauditing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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