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Record W2123776321 · doi:10.1109/securware.2009.43

A PIN Entry Scheme Resistant to Recording-Based Shoulder-Surfing

2009· article· en· W2123776321 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 institutionsConcordia University
Fundersnot available
KeywordsLoginComputer scienceScheme (mathematics)Authentication (law)Computer securityIdentification (biology)Smart cardPoint (geometry)

Abstract

fetched live from OpenAlex

Two-factor authentication techniques using combination of magnetic cards and personal identification numbers (PINs) are widely used in many applications including automatic teller machines and point of sales. Similar to other valuable personal possessions, cards can be easily stolen by pickpockets. Furthermore, recent security reports show that magnetic cards can be easily duplicated using fake card readers and PINs can be obtained by shoulder surfing legitimate users' PIN entry processes. With this combination, criminals can easily break into users' accounts which represents a great threat. In this paper, we propose a new PIN entry scheme which is resistant against shoulder-surfing attacks conducted by shoulder-surfers with normal cognitive capabilities. Additionally, this scheme offers a relatively good level of security when the shoulder-surfer can record the entire login procedure for one or two times with a video device. Mathematical analysis of the proposed scheme is also presented.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.922
Threshold uncertainty score0.400

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.023
GPT teacher head0.272
Teacher spread0.249 · 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

Citations25
Published2009
Admission routes1
Has abstractyes

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