Post-Quantum Secure Protocol for Confidential and Auditable Data Transmission
Bibliographic record
Abstract
The advent of quantum computing poses a significant threat to the cryptographic foundations of current electronic voting (e-voting) systems, which commonly rely on algorithms such as Rivest-Shamir-Adleman (RSA) and Elliptic Curve Cryptography (ECC). These algorithms are believed to be vulnerable to attacks imposed by quantum computers, jeopardizing core e-voting properties, including ballot secrecy, integrity, and auditability. To address these challenges, this work presents a quantum-resistant electronic-voting (e-voting) system, named post-quantum e-voting system (PQEVS) built entirely upon cryptographic primitives standardized by the National Institute of Standards and Technology (NIST) for post-quantum security. The proposed PQEVS utilizes Dilithium for secure voter authentication, Brakerski/Fan-Vercauteren (BFV)-based Fully Homomorphic Encryption (FHE) for privacy-preserving vote tallying, and Picnic-based Zero-Knowledge Proofs (ZKPs) to ensure vote validity without compromising voter anonymity. Designed for modularity and scalability, our PQEVS delivers enhanced security while achieving significant performance gains, reducing vote processing latency by 85% and supporting throughputs of up to 36,000 votes per second. These results highlight the practicality and robustness of post-quantum cryptography in securing large-scale electoral processes, setting a new benchmark for verifiable and future-proof e-voting systems.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".