On the Impact of Touch ID on iPhone Passcodes
Bibliographic record
Abstract
Smartphones today store large amounts of data that can be confidential, private or sensitive. To protect such data, all mobile OSs have a phone lock mechanism, a mechanism that requires user authentication before granting access to applications and data on the phone. iPhone's unlocking secret (a.k.a., passcode in Apple's terminology) is also used to derive a key for encrypting data on the device. Recently, Apple has introduced Touch ID, that allows a fingerprint-based authentication to be used for unlocking an iPhone. The intuition behind the technology was that its usability would allow users to use stronger passcodes for locking their iOS devices, without substantially sacrificing usability. To this date, it is unclear, however, if users take advantage of Touch ID technology and if they, indeed, employ stronger passcodes. It is the main objective and the contribution of this paper to fill this knowledge gap. In order to answer this question, we conducted three user studies (a) an in-person survey with 90 participants, (b) interviews with 21 participants, and (c) an online survey with 374 Amazon Mechanical Turks. Overall, we found that users do not take an advantage of Touch ID and use weak unlocking secrets, mainly 4-digit PINs, similarly to those users who do not use Touch ID. To our surprise, we found that more than 30% of the participants in each group did not know that they could use passwords instead of 4-digit PINs. Some other participants indicated that they adopted PINs due to better usability, in comparison to passwords. Most of the participants agreed that Touch ID, indeed, offers usability benefits, such as convenience, speed and ease of use. Finally, we found that there is a disconnect between users' desires for security that their passcodes have to offer and the reality. In particular, only 12% of participants correctly estimated the security their passcodes provide.
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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 | 0.007 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".