Zonedroid: control your droid through application zoning
Bibliographic record
Abstract
Research has shown that the android permission model was insufficient for providing protection against malicious behaviors of the untrusted third-party applications. To improve this scenario, Google modified the permission model in the recent Android version. However, in our analysis, it is still not an ideal option to enforce fine-grained access control. In this paper, we propose an extension and implementation of the Android permission model, ZoneDroid, to control a set of applications easily by creating multiple application zones (i.e., application groups). It is an approach to control application groups by modifying the Android permission model. All other previous approaches focused on restricting individual applications or creating separate user profiles. ZoneDroid minimizes security and privacy risks with a finer granularity of restrictions. Users can also control multiple devices using the cloud. Different zones (high privilege, trusted, new, restricted, etc.) have different runtime policies and enforce fine-grained access control. The ability to control application groups efficiently can be a valuable addition to the existing Android permission model. Experiments show that ZoneDroid is effective against information leak and it can protect the device from becoming a part of a botnet. ZoneDroid offers much less user action when controlling multiple applications and its performance overhead is negligible.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".