On Cipher-Dependent Related-Key Attacks in the Ideal-Cipher Model.
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Bibliographic record
Abstract
Abstract. Bellare and Kohno introduced a formal framework for the study of related-key attacks against blockciphers. They established sufficient conditions (output-unpredictability and collision-resistance) on the set of relatedkey-deriving (RKD) functions under which an ideal cipher is secure against related-key attacks, and suggested this could be used to derive security goals for real blockciphers. However, to do so requires the reinterpretation of results proven in the ideal-cipher model for the standard model (in which a blockcipher is modelled as, say, a pseudorandom permutation family). As we show here, this is a fraught activity. In particular, building on a recent idea of Bernstein, we first demonstrate a related-key attack that applies generically to a large class of blockciphers. The attack exploits the existence of a short description of the blockcipher, and so does not apply in the ideal-cipher model. However, the specific RKD functions used in the attack are provably output-unpredictable and collisionresistant. In this sense, the attack can be seen as a separation between the ideal-cipher model and the standard model. Second, we investigate how the related-key attack model of Bellare and Kohno can be extended to include sets of RKD functions that themselves access the ideal cipher. Precisely such related-key functions underlie the generic attack, so our extended modelling allows us to capture a larger universe of related-key attacks in the idealcipher model. We establish a new set of conditions on related-key functions that is sufficient to prove a theorem analogous to the main result of Bellare and Kohno, but for our extended model. We then exhibit non-trivial classes
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.000 | 0.003 |
| 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