Bibliographic record
Abstract
Android devices use volume encryption to protect private data storage. While this paradigm has been widely adopted for safeguarding PC storage, the always-on mobile usage model makes volume encryption a weaker proposition for data confidentiality on mobile devices. PCs are routinely shut down which effectively secures private data and encryption keys. Mobile devices, on the other hand, typically remain powered-on for long periods and rely on a lock-screen for protection. This leaves lock-screen protection, something routinely bypassed, as the only barrier securing private data and encryption keys. Users are unlikely to embrace a practice of shutting down their mobile phones, as it impairs their communication and computing abilities. We propose Deadbolt: a method for maintaining most mobile computing functionality, while offering the security benefits of a powered off device with respect to storage encryption. Deadbolt prevents access to internal storage even if the adversary can exploit a lock screen bypass vulnerability or perform a cold boot attack. Users can gracefully switch between the Deadbolt and unlocked modes in less time than a system reboot. Deadbolt offers the additional benefit of an incognito environment in which logs and actions will not be recorded.
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.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.042 |
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".