CAPEF: Context-Aware Policy Enforcement Framework for Android Applications
Bibliographic record
Abstract
The notion of Context-Awareness of mobile applications is drawing more attention, where many applications need to adapt to physical environments of users and devices, such as location, time, connectivity, resources, etc. While these adaptive features can facilitate better communication and help users to access their information anywhere at any time, this however bring risks caused by the potential loss, misuse, or leak of users’ confidential information. Therefore, a flexible policy-based access control system is needed to monitor critical functions executed by Android applications, especially, those requiring access to user’s sensitive and crucial information. This paper introduces CAPEF, which is a policy specification framework that enforces context-aware inter-app security policies to mitigate privacy leakage across different Android applications. It also, provides an instrumentation framework to effectively enforce different behaviors based on automated context-aware policies to each Android application individually without modifying the underlying platform. Accordingly, the modified applications will be forced to communicate with our centralized policy engine to avoid any malware collusion that occur without the users’ awareness. Experiments conducted on CAPEF shows an effective performance on the size of the enforced application after the instrumentation. The average size added was 705 bytes, which is about 0.063% of the size of the original applications, which is significantly small compared to other existing enforcement approaches. Also, we have denoted that the size and the execution time of the policy increases whenever the policies become more complex.
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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".