Overcoming Bottlenecks in Homomorphic Encryption for the 2024 Mexican Federal Election
Bibliographic record
Abstract
On June 2, 2024, Mexico held its federal elections. The majority of Mexican citizens voted in person at the polls in this historic election. For the first time though, Mexican citizens living outside their country were able to vote online via a web app, either on a personal device or using an electronic voting kiosk at one of 23 embassies and consulates in the U.S., Canada, and Europe. In total, 144,734 people voted outside of Mexico: 122,496 on a personal device and 22,238 in-person at a kiosk. Voting was open for remote voting from 8PM, May 18, 2024 to 6PM, June 2, 2024 and was open for in-person voting from 8AM-6PM on June 2, 2024. This article describes the technical and cryptographic tools applied to secure the ex-patriate component of the election and to enable INE (Mexico's National Electoral Institute) to generate provable election results within minutes of the close of the election. This article will also describe how the solutions we present scale to elections on a national level.
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.
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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".