A Review on Distributed Blockchain Technology for E-voting Systems
Bibliographic record
Abstract
Abstract Election is an important event in all countries. Conventional voting suffers many issues, such as cost of time and efforts needed for tallying and counting results, cost in papers, arrangements and all that it takes for a voting process to be achieved. Many countries such as Australia, Belgium, Brazil, Canada, Estonia, France, Germany, India, Italy, Namibia, the Netherlands, Norway, Peru, Switzerland, the UK, Venezuela and the Philippines considered online e-voting systems, but the traditional e-voting systems suffer a lack of trust, it is not known if a vote is counted correctly, tampered or not. The voter has no guarantee that his/her vote is considered as they voted in elections, it’s a lack of transparency. A solution is e-voting systems based on blockchain (sometimes referred as Distributed Ledger Technology (DLT)) has now turned to be promising for what properties it offer, such as, privacy, security, transparency, accuracy, decentralization in which no central control exist, and most of all, creates an immutable system, where citizens are allowed to vote from their location by using digital devices (smart phones, computers, electronic voting machines). Also, due to the COVID-19 pandemic, many technology applications are heading towards systems with all these properties, at the same time, maintaining social distancing. This review introduced many different ideas for implementing e-voting systems based on Blockchain and how the users (voters and candidates) interact with the system showing the voting process from the first step of registration to authentication till showing the final results. At the end of this review we will illustrate a table that contain all mechanisms used in the papers involved that covers the most important requirements needed for every e-voting system based on blockchain or Distributed Ledger Technology (DLT).
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".