On Cipher-Dependent Related-Key Attacks in the Ideal-Cipher Model.
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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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.003 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".