Eperio: Mitigating Technical Complexity in Cryptographic Election Verification.
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
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| 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