Proof-carrying cloud computation: The case of convex optimization
Bibliographic record
Abstract
Cloud computing provides a “pay-per-use” utility service which offers the customer the economical access to large amount of computing resources with minimal management overhead. Despite the tremendous benefits, computation outsourcing also eliminates the customer's ultimate control over the data computation process, which makes securing cloud computation an imperative and challenging task, especially in the aspect of integrity verification. To address these challenges, in this paper we propose to research on integrity verification mechanisms for secure outsourced computations in cloud computing. In particular, we focus on outsourcing the widely applicable engineering optimization problem, i.e., convex optimization, and aim to investigate efficient integrity verification mechanisms using application-specific techniques. Our security design does not require the use of heavy cryptographic tools. Instead, we leverage the inherent structure of the optimization problems and the proof-carrying characteristics of the solving algorithms to achieve efficient integrity verification. The proposed design provides substantial computational savings on the customer side and introduce marginal overhead on the cloud side. We further prove its correctness and soundness. The extensive experiments under real cloud environment show our mechanisms ensure strong integrity assurance with high efficiency on both the customer and cloud sides and are readily applicable in practice.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".