AppArmor For Health Data Access Control: Assessing Risks and Benefits
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
The AppArmor Linux Security Module (LSM) is widely used on Linux operating systems as it, among other things, provides mandatory access control (MAC) and isolates processes. This isolation helps meet the privacy requirements for critical applications. These application security policies are defined with profiles loaded into the Linux system kernel. However, these access control mechanisms are far from covering all the rising demands for confidentiality enforcement regarding critical applications. This paper conducts a risks and benefits analysis to assess whether a healthcare infrastructure can safely rely on the AppArmor LSM to protect its sensitive data. Thus, the general architecture of AppAmor comes to be detailed. Then, a static code analysis is performed to study the data structures found in the LSM. Finally, the outbreak of would-be side-channel attacks from userspace is discussed while offering mitigation methods. The result of this analysis shows that the AppArmor LSM is susceptible to side-channel attacks and should be used as part of a more comprehensive defense-in-depth strategy.
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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it