Summative Usability Assessments of STAR-Vote: A Cryptographically Secure e2e Voting System That Has Been Empirically Proven to Be Easy to Use
Bibliographic record
Abstract
BACKGROUND: From the project's inception, STAR-Vote was intended to be one of the first usable, end-to-end (e2e) voting systems with sophisticated security. To realize STAR-Vote, computer security experts, statistical auditors, human factors (HF)/human-computer interaction (HCI) researchers, and election officials collaborated throughout the project and relied upon a user-centered, iterative design and development process, which included human factors research and usability testing, to make certain the system would be both usable and secure. OBJECTIVE: While best practices in HF/HCI methods for design were used and all apparent usability problems were identified and fixed, summative system usability assessments were conducted toward the end of the user-centered design process to determine whether STAR-Vote is in fact easy to use. METHOD AND RESULTS: After collecting efficiency, effectiveness, and satisfaction measurements per ISO 9241-11's system usability criteria, an analysis of the data revealed that there is evidence for STAR-Vote being the most usable, cryptographically secure voting system to date when compared with the previously tested e2e systems: Helios, Prêt à Voter, and Scantegrity. CONCLUSION AND APPLICATION: is a significant accomplishment, because tamper-resistant voting systems can be used in U.S. elections to ensure the integrity of the electoral process, while still ensuring that voter intent is accurately reflected in the cast ballots. Moreover, this research empirically shows that a complex, secure system can still be usable-meaning that implemented security is not an excuse for poor usability.
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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 | 0.029 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".