Usability and Security Perceptions of Implicit Authentication: Convenient, Secure, Sometimes Annoying.
Bibliographic record
Abstract
Implicit authentication (IA) uses behavioural biometrics to provide continuous authentication on smartphones. IA has been advocated as more usable when compared to traditional explicit authentication schemes, albeit with some security limitations. Consequently researchers have proposed that IA provides a middle-ground for people who do not use traditional authentication due to its usability limitations or as a second line of defence for users who already use authentication. However, there is a lack of empirical evidence that establishes the usability superiority of IA and its security perceptions. We report on the first extensive two-part study (n = 37) consisting of a controlled lab experiment and a field study to gain insights into usability and security perceptions of IA. Our findings indicate that 91% of participants found IA to be convenient (26% more than the explicit authentication schemes tested) and 81% perceived the provided level of protection to be satisfactory. While this is encouraging, false rejects with IA were a source of annoyance for 35% of the participants and false accepts and detection delay were prime security concerns for 27% and 22% of the participants, respectively. We point out these and other barriers to the adoption of IA and suggest directions to overcome them.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".