Design and implementation of a cryptographically secure electronic voting infrastructure
Bibliographic record
Abstract
Cyber security is the application of technologies, processes, and controls to protect against attacks on confidentiality, integrity, and availability. Cryptography maintains confidentiality by securing communications from being intercepted, provides integrity by preventing unauthorized modification of data, and provides availability by allowing data to be transmitted securely. There is currently limited to no application of cryptographic controls at election sites in today's voting environment due to the use of legacy systems and paper systems that do not support the technology required for encryption. This paper proposes an electronic voting solution to mitigate risk through the design and implementation of a secure, electronic voting app and infrastructure. Here, we present evidence, using a thorough National Institute of Standards and Technology (NIST) risk assessment, that removing human interaction remediates vulnerabilities within today’s infrastructure and mitigates overall risk. We also extract multiple NIST Special Publication 800-53 family controls to analyze the vulnerabilities in today’s voting infrastructure. Using our proposed secure electronic infrastructure, we mitigate the risk inherent in today’s election environment, and we propose a model to secure our democracy and the future prospect of voting electronically.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".