Eperio: Mitigating Technical Complexity in Cryptographic Election Verification.
Bibliographic record
Abstract
Cryptographic (or end-to-end) election verification is a promising approach to providing transparent elections in an age of electronic voting technology. In terms of execution time and software complexity however, the technical requirements for conducting a cryptographic election audit can be prohibitive. In an effort to reduce these requirements we present Eperio: a new, provably secure construction for providing a tally that can be efficiently verified using only a small set of primitives. We show how common-place utilities, like the use of file encryption, can further simplify the verification process for election auditors. Using Python, verification code can be expressed in 50 lines of code. Compared to other proposed proofverification methods for end-to-end election audits, Eperio lowers the technical requirements in terms of execution time, data download times, and code size. As an interesting alternative, we explain how verification can be implemented using True-Crypt and the built-in functions of a spreadsheet, making Eperio the first end-to-end system to not require special-purpose verification software. 1
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".