Verifiable Computation using Smart Contracts
Bibliographic record
Abstract
Outsourcing computation has been widely used to allow weak clients to access computational resources of a cloud. A natural security requirement for the client is to be able to efficiently verify the received computation result. An attractive approach to verifying a general computation is to send the computation to multiple clouds, and use carefully designed protocols to compare the results and achieve verifiability. This however requires a Trusted Third Party (TTP) to manage the interactions of the client and the clouds. Our goal is to employ a smart contract to act as the TTP. This also relieves the client from directly interacting with the clouds, and engaging in possibly a complex stateful protocol. We focus on a verifiable computation protocol of Canetti, Riva and Rothbulm (CRR) with provable security against a malicious cloud, and show that direct employment of the protocol with a smart contract will result in an attack that will undermine the security of the system. We describe and analyze the attack, and extend CRR protocol to protect against this attack, resulting in a secure verifiable computation system using smart contracts. We also give the pseudocode of a smart contract and the required functions that can be used to implement the protocol, written in the Solidity language, and explain its working.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".