Usability and security evaluation of GeoPass
Bibliographic record
Abstract
We design, implement, and evaluate GeoPass: an interface for digital map-based authentication where a user chooses a place as his or her password (i.e., a "location-password"). We conducted a multi-session in-lab/at-home user study to evaluate the usability, memorability, and security of location-passwords created with GeoPass. The results of our user study found that 97% of users were able to remember their location-password over the span of 8-9 days and most without any failed login attempts. Users generally welcomed GeoPass; all of the users who completed the study reported that they would at least consider using GeoPass for some of their accounts. We also perform an in-depth usability and security analysis of location-passwords. Our security analysis includes the effect of information that could be gleaned from social engineering. The results of our security analysis show that location-passwords created with GeoPass can have reasonable security against online attacks, even when accounting for social engineering attacks. Based on our results, we suggest GeoPass would be most appropriate in contexts where logins occur infrequently, e.g., as an alternative to secondary authentication methods used for password resets, or for infrequently used online accounts.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".