Verifiable local computation on distributed data
Bibliographic record
Abstract
Outsourcing of storage and computation from a resource-restricted client to a powerful cloud raises many security issues such as the privacy of the data and the integrity of any delegated computation on the outsourced data. While many techniques have been introduced to protect the client's data privacy or computation integrity, achieving both of them is challenging. In this paper, we propose a multi-server verifiable local computation (VLC) model where the client can privately outsource data blocks m=(m1, ..., mn) to cloud servers and later verify computations on any portion of the outsourced data. We propose two constructions of multi-server VLC schemes. Our schemes achieve data privacy in the sense that no collusion of a subset (size less than a threshold) of the cloud servers can learn any information about $m$; and computation integrity in the sense that no collusion of a subset (size less than a threshold) of the cloud servers can cause the client to accept their incorrect answers. Our first construction is solely based on PRF and very efficient; our second construction uses bilinear maps and achieves amortized closed-form efficiency over multiple computations of a function.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".