Securing E-Petition: A Privacy-Preserving Fine-Grained Electronic Petition System for Health and Political Petitions
Bibliographic record
Abstract
E-petition has played an important role in health and politics that collects public opinions and requests a superior or an authority to take actions towards a health or political problem. However, this activity exposes the privacy of the signers who participate to express opinions. In this paper, we propose a privacy-preserving fine-grained e-petition system that supports attribute-based identity verification for signers, while protecting their privacy. By considering the target groups of signers in a specific health or political petition, an attribute policy is defined to ensure that only the signers with the attributes that satisfy the attribute policy can sign the petition. The fine-grained petition is better than the traditional e-petitions because it can improve the trustworthiness of the petition results via proactive signer selection. Moreover, the new petition system protects the identities of the signers by using the non-interactive zero-knowledge proof system, such that the signers are anonymous in signing petitions. In addition, the proposed petition system supports the tracing of double-signing, a cheating behavior that an anonymous signer can submit more than one signature in a petition without being detected. Finally, we prove that the proposed petition system achieves the desirable security properties, including anonymity, unforgeability, and traceability, and demonstrate that the system is efficient to be implemented on the mobile devices.
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 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".