The Effectiveness of Receipt-Based Attacks on ThreeBallot
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The ThreeBallot voting system is an end-to-end voter-verifiable voting system. Each voter fills out three ballots according to a few simple rules and takes a copy of one of them home as a receipt for verification purposes. All ballots are posted on a public bulletin board so that any voter may verify the result. In this paper, we provide the first steps toward investigating the effectiveness of attacks using the voter's receipt and the bulletin board, using a theoretical rather than simulation-based approach. Focusing on two-candidate races, we determine thresholds for when a voter's vote can be reconstructed from their receipt, and when a coercer can effectively verify if a voter followed instructions by looking for prespecified patterns on the bulletin board. Combining these two results allows us to determine safe ballot sizes that resist known attacks. We also generalize a previous observation that an individual receipt can leak information about a voter's choices.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it