Usability and Security Perceptions of Implicit Authentication: Convenient, Secure, Sometimes Annoying.
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it