An Efficient Blockchain-Based Self-Tallying Voting Protocol with Full-Anonymity
Bibliographic record
Abstract
Abstract As an electronic form of traditional voting, electronic voting is becoming more and more popular in today’s information society. Most of the existing electronic voting protocols need a trusted center to calculate the voting result, but the requirement of a trusted center is often unrealistic and prone to single point of failure. In this regard, the decentralized electronic voting protocols based on blockchain have been proposed. Unfortunately, most existing blockchain-based voting protocols fail to ensure anonymity, legitimacy, and correctness of counting. Besides, they do not satisfy robustness, i.e., the voting result cannot be counted in the event of voter abstention. To address the above challenges, we propose a novel blockchain-based self-tallying voting protocol, where the group signature and zero-knowledge proof are utilized in a way that the voter can securely distribute anonymous and unlinkable electronic ballots, thereby guaranteeing complete anonymity and legitimacy. Meanwhile, a novel signcryption algorithm is designed by combining distributed ElGamal encryption and Paillier encryption algorithms, which enhances the computational efficiency of voting results while supporting robustness. The security proof shows that our protocol ensures the confidentiality of ballots, complete anonymity, legitimacy, fairness, dispute-freeness and resistance against multi-voting. In addition, our protocol satisfies robustness, i.e., voting result can be correctly calculated and verified even if some voters abstain from voting. Finally, extensive experiments show that our protocol greatly reduces the computational cost and communication overhead, and is more practical than existing self-tallying voting protocols.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.003 |
| 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".