Preventing cache-based side-channel attacks in a cloud environment
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
Cloud computing is a unique technique for outsourcing and aggregating computational hardware needs. By abstracting the underlying machines cloud computing is able to share resources among multiple mutually distrusting clients. While there are numerous practical benefits to this system, this kind of resource sharing enables new forms of information leakage such as hardware side-channels. In this paper, we investigate the usage of CPU-cache based side-channels in the cloud and how they compare to traditional side-channel attacks. We go on to demonstrate that new techniques are necessary to mitigate these sorts of attacks in a cloud environment, and specify the requirements for such solutions. Finally, we design and implement two new cache-based side-channel mitigation techniques, implementing them in a state-of-the-art cloud system, and testing them against traditional cloud technology.
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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 | 0.001 | 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 | 0.001 |
| 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