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Record W3000544438 · doi:10.1109/pst47121.2019.8949033

Geographic Hints for Passphrase Authentication

2019· article· en· W3000544438 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicUser Authentication and Security Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsLoginUsabilityComputer scienceAuthentication (law)RecallSession (web analytics)World Wide WebInformation retrievalHuman–computer interactionComputer security

Abstract

fetched live from OpenAlex

We propose and study the use of geographic hints to aid memorability of passphrase-style authentication secrets. Geographic hints are map locations that are selected by the user at the time of passphrase creation, and shown to the user as a hint at the time of passphrase login. We implement the GeoHints system and analyze how geographic hints impact the usability and security of passphrase-style secrets in a multi-session user study (n=38). The study involved testing for multiple passphrase interference-each participant was asked to recall 4 distinct passphrases. Our study indicates that while geographic hints showed promise for reducing memory interference, GeoHints (as implemented) does not produce a viable authentication system, as the login success rate was 25% 7-11 days after passphrase selection. We analyze the root causes of login errors, finding that most were due to inexact recall of free-form text input. This finding points towards opportunities to improve the system design, and we suggest improvements that we believe will lead to viable systems that employ geographic hints.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.922
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.009
GPT teacher head0.235
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations5
Published2019
Admission routes1
Has abstractyes

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