Survey On Secure E-Voting Platform
Bibliographic record
Abstract
E-voting systems are increasingly being explored with the aim of taking over from traditional paper-based voting and may offer several advantages, including efficiency, accessibility, and cost-cutting. These systems include DRE machines, internet-based voting, to even blockchain-enabled voting solutions. However, this shift toward e-voting is attended by a raft of significant hurdles on security, secrecy in voting, dependability of the systems, and scalability. This survey paper covers the current status of e-voting systems and related work and literature regarding the design, implementation, and evaluation. These topics will range from security vulnerabilities regarding DRE and Internet voting to privacy and anonymity concerns with respect to maintaining confidentiality of voters, accessibility and usability of e-voting platforms for the diverse voter population, and the increasingly emerging blockchain technology that provides secure and transparent processes toward voting. The survey underlines important gaps in present research and points to a number of future directions that might help in building up integrity, transparency, and trustworthiness in e-voting systems. In overcoming the challenges lying ahead, e-voting has the potential to significantly improve democratic processes and enhance voter participation. INDEX TERMS : Online Voting , Secure Voting Platform, Voter Authentication, OTP Verification, SMS Gateway, Encryption, Web Application Development, Voting Results , Election Management System
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".