Revisiting Silent Coercion
Bibliographic record
Abstract
Abstract We revisit “silent coercion” where an adversary gains access to a voter’s credential without the voter’s knowledge in an E2E verifiable, coercion-resistant Internet voting system. We argue that in this setting, casting an intended vote is impossible since the cryptographic backend can no longer distinguish the voter and adversary. However, we affirm that the voter can still act to nullify adversarial ballots, which is preferable to inaction. We provide a new instantiation of nullification using zero-knowledge proofs and multiparty computation, which improves on the efficiency of the current state-of-the-art. We also demonstrate an example voting system—VoteXX—that uses nullification. Our nullification protocol can complement new and existing techniques for coercion resistance (which all require voters to hide cryptographic keys from the coercer), providing a failsafe option for voters whose keys leak.
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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".