A Server-Side Solution to Cache-Based Side-Channel Attacks in the Cloud
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
As Cloud services become more common place, recent work have uncovered vulnerabilities unique to Cloud systems. Specifically, the paradigm promotes a risk of information leakage across virtual machine isolation via side-channels. In this paper, we investigate the current state of side-channel vulnerabilities involving the CPU cache, and identify the shortcomings of traditional defenses in a Cloud environment. We explore why solutions to non-Cloud cache-based side-channels cease to work in Cloud environments, and develop a mitigation technique applicable for Cloud security. Applying this solution to a canonical Cloud environment, we demonstrate the validity of this Cloud-specific, cache-based side-channel mitigation technique. Furthermore, we show that it can be implemented as a server-side approach to improve security without inconveniencing the client. Finally, we conduct a comparison of our solution to the current state-of-the-art.
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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 | 0.000 | 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 | 0.001 |
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