On Protecting Cryptographic Keys Against Continual Leakage.
Bibliographic record
Abstract
Side-channel attacks have often proven to have a devastating effect on the security of cryptographic schemes. In this paper, we address the problem of storing cryptographic keys and computing on them in a manner that preserves security even when the adversary is able to obtain information leakage during the computation on the key. Using the recently achieved fully homomorphic encryption, we show how to encapsulate a key and repeatedly evaluate arbitrary functions on it so that no adversary can gain any useful information from a large class of side-channel attacks. We work in the model of Micali and Reyzin, assuming that only the active part of memory during computation leaks information. Similarly to previous works, our construc-tion makes use of a single “leak-free ” hardware token that samples from a globally-fixed distribution that does not depend on the key. Our construction is the first general compiler to achieve resilience against polytime leakage functions without performing any leak-free computation on the underlying secret key. Furthermore, the amount of computation our construction must perform does not grow with the amount of leakage the adver-sary is able to obtain; instead, it suffices to make a stronger assumption about the security of the fully homomorphic encryption. 1
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.001 | 0.009 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".