Refereed Delegation of Computation Using Smart Contracts
Bibliographic record
Abstract
Outsourcing computation enables a weak client to expand its computational power as the need arises. A basic requirement of outsourcing computation is the guarantee that the computation result is correct. Cryptographic solutions that provide verifiability for the computation result when the computation is outsourced to a single server, are complex and fragile. We consider the intuitive approach of verifiable computation, called verifiable computation by replication, when the computation is replicated on multiple servers, and a referee decides the result of the final computation using the outputs of all servers. We consider the case when a smart contact is used as the referee. We propose a security model in the Universal Composability (UC) framework of Canetti, and design a 2-server and an n-server protocol with proved security in our model. Our protocols build on the Refereed Delegation of Computation (RDoC) framework of Canetti, Riva, and Rothblum, underline the challenges of using a smart contract as a referee, and address those challenges in the designed protocols. We give the efficiency analysis of the protocols, provide a proof of concept implementation for our protocols using Ethereum smart contact, and give concrete cost values for an example computation.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.007 |
| 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".