Blockchain-Based E-Voting Systems: A Systematic Literature Review on Privacy, Integrity, and Scalability
Bibliographic record
Abstract
Blockchain technology has been envisioned as an emerging facilitator of auditable, transparent, and secure electronic voting (e-voting) systems to overcome issues with traditional and electronic voting systems. However, preserving data integrity, offering voter privacy, and scalability in blockchainbased e-voting systems are persistent issues. In this systematic literature review of peer-reviewed research articles from 2018 to 2025, this paper explores cryptographic schemes, architecture designs for blockchain-based e-voting systems, and solutions for scalability. By taking an PRISMA-congruent structured research methodology approach, nine core studies are reviewed to discuss Zero Knowledge Proofs and blind signature schemes for maintaining privacy conservation, blockchain immutability to maintain integrity, and layer-2 scaling solutions to bypass throughput bottlenecks. Conclusions suggest that although transparency and audita-bility are elevated with applications of blockchain technology, implementation for massive-scale elections remains in its nascent stage and requires development in privacypreservation cryptographic schemes and scalable architecture designs. As a review paper, it compiles an updated summary of the status of the landscape of blockchain-based e-voting systems and highlights existing knowledge gaps and proposes research directions for developing secure, scalable, and privacy-respecting digital elections.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".