Information-Theoretically Secure Voting Without an Honest Majority
Bibliographic record
Abstract
Abstract. We present three voting protocols with unconditional privacy and information-theoretic correctness, without assuming any bound on the number of corrupt voters or voting authorities. All protocols have polynomial complexity and require private channels and a simultaneous broadcast channel. Our first protocol is a basic voting scheme which allows voters to interact in order to compute the tally. Privacy of the ballot is unconditional, but any voter can cause the protocol to fail, in which case information about the tally may nevertheless transpire. Our second protocol introduces voting authorities which allow the implementation of the first protocol, while reducing the interaction and limiting it to be only between voters and authorities and among the authorities themselves. The simultaneous broadcast is also limited to the authorities. As long as a single authority is honest, the privacy is unconditional, however, a single corrupt authority or a single corrupt voter can cause the protocol to fail. Our final protocol provides a safeguard against corrupt voters by enabling a verification technique to allow the authorities to revoke incorrect votes. We also discuss the implementation of a simultaneous broadcast channel with the use of temporary computational assumptions, yielding versions of our protocols achieving everlasting security.
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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 | 0.005 | 0.012 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".