Enhancing Security in Online Voting Systems: A Cryptographic Approach Utilizing Galois Fields
Bibliographic record
Abstract
Ensuring information security is indispensable during data communication among a collective of entities.This requirement is exemplified in the context of online voting systems (OVS), which necessitate the conduction of fair and transparent elections.A pivotal aspect of securing the OVS involves authenticating authorized voters prior to vote casting and encrypting the votes before their transfer over a secure channel for tallying.The present study centers on the development of a mathematical model for an authentication scheme that can be implemented in an OVS to facilitate impartial elections.The devised model integrates mathematical and cryptographic principles of Galois fields, group codes, and pseudo-random key stream generators to formulate individual voter passcodes, thereby providing two-factor authentication.The proposed scheme is exemplified through a scenario suitable for orchestrating a medium-scale election involving 65,536 voters via an OVS.Furthermore, with the appropriate selection of inputs, the model exhibits the capacity to support large-scale elections.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".