A Lightweight Ethereum Voting Prototype for Hospital Ethics Committees with Receipt-Based Inclusion Verification
Researchers prototype Ethereum voting for hospital ethics committees with public receipts but no secret ballots.
A cs.CR paper presents a Solidity, Hardhat, React, MetaMask, and ethers.js prototype for hospital ethics committee voting. Role controls, case-state checks, duplicate-vote controls, and a receipt hash support public audit and transaction inclusion verification. Because vote events expose wallet addresses and vote values, the design is pseudonymous rather than anonymous, secret, receipt-free, or coercion-resistant. Evaluation reports 22 passing functional tests and 284,137 gas per vote on local Hardhat; a 12-participant simulation used assumed probabilities and is not human-subject evidence.
- Prototype uses Solidity, Hardhat, React, MetaMask, and ethers.js.
- Receipts support inclusion checks but are not coercion-resistant.
- On-chain events reveal wallet addresses and vote values.
- Twenty-two tests pass; each vote uses 284,137 gas locally.
Full article124 words · extracted from arxiv.org · click to collapse
This paper presents a Solidity, Hardhat, React, MetaMask, and ethers.js prototype for hospital ethics committee voting. Role controls, case-state checks, duplicate vote controls, and a receipt hash support public audit and transaction inclusion verification. Because vote events expose wallet addresses and vote values, the design provides pseudonymous auditability, not anonymous or secret-ballot voting; the receipt is neither receipt-free nor coercion-resistant. Evaluation reports 22 passing functional tests and local Hardhat gas use, including 284,137 gas per vote. A 12-participant simulation used assumed probabilities and is not human-subject evidence. Residual risks include multiple wallets, administrator or frontend compromise, credential reassignment, front-running, denial of service, and untested adversarial paths. Confidential deployment requires governed enrollment, encrypted ballots, independent audit, adversarial testing, reproducible benchmarks, and a real user study.
Text extracted automatically; images, tables and formatting may be missing. Original: https://arxiv.org/abs/2609.29981