Bibliographic record
Abstract
Rising concerns about mobile security have motivated the use of architectural features such as ARM TrustZone to protect sensitive applications from compromise by malicious applications or a compromised OS. However, many TEE OSes (which run in TrustZone) currently assume all applications in TrustZone are trusted, and thus do not provide strong isolation guarantees between them. The benefit of this is that TEE OSes can be simple, allowing them to provide a high-assurance trusted computing base (TCB). However, unlike how arbitrary third-party mobile applications can be installed onto a smartphone, the need for mutual trust among all applications running inside TrustZone prevents the installation of 3rd party applications on the TEE OS. In this paper, we identify the key properties that define application code that may wish to use TrustZone and show that a standard TEE OS can be extended to support multiple, mutually distrusting applications in TrustZone with less than a 3% increase in the TCB. We realize our ideas in Pearl-TEE, a novel TEE OS prototype we have implemented that can provide mechanisms specific to the needs of TrustZone applications, including isolation for execution, secure persistent storage, and support for network communication. We find that Pearl-TEE imposes less than 20% performance overhead on applications.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.072 | 0.040 |
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".