Know Your Enemy: The Risk of Unauthorized Access in Smartphones by Insiders
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
Smartphones store large amounts of sensitive data, such as SMS messages, photos, or email. In this paper, we report the results of a study investigating users' concerns about unauthorized data access on their smartphones (22 interviewed and 724 surveyed subjects). We found that users are generally concerned about insiders (e.g., friends) accessing their data on smartphones. Furthermore, we present the first evidence that the insider threat is a real problem impacting smartphone users. In particular, 12% of subjects reported a negative experience with unauthorized access. We also found that younger users are at higher risk of experiencing unauthorized access. Based on our results, we propose a stronger adversarial model that incorporates the insider threat. To better reflect users' concerns and risks, a stronger adversarial model must be considered during the design and evaluation of data protection systems and authentication methods for smartphones.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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