Quantifying and Mitigating Cache Side Channel Leakage with Differential Set
Bibliographic record
Abstract
Cache side-channel attacks leverage secret-dependent footprints in CPU cache to steal confidential information, such as encryption keys. Due to the lack of a proper abstraction for reasoning about cache side channels, existing static program analysis tools that can quantify or mitigate cache side channels are built on very different kinds of abstractions. As a consequence, it is hard to bridge advances in quantification and mitigation research. Moreover, existing abstractions lead to imprecise results. In this paper, we present a novel abstraction, called differential set, for analyzing cache side channels at compile time. A distinguishing feature of differential sets is that it allows compositional and precise reasoning about cache side channels. Moreover, it is the first abstraction that carries sufficient information for both side channel quantification and mitigation. Based on this new abstraction, we develop a static analysis tool DSA that automatically quantifies and mitigates cache side channel leakage at the same time. Experimental evaluation on a set of commonly used benchmarks shows that DSA can produce more precise leakage bound as well as mitigated code with fewer memory footprints, when compared with state-of-the-art tools that only quantify or mitigate cache side channel leakage.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".