The Security of RC6 against Asymmetric Chi-square Test Attack
Bibliographic record
Abstract
Knudsen and Meier applied the χ2-attack to RC6. The χ2-attack recovers a key by using high correlations measured by χ2-value. The best χ2-attacks to RC6 whose security is guaranteed theoretically works on 16-round RC6 with 192- and 256-bit key but just 8-round RC6 with 128-bit key, because it recovers keys of RC6 symmetrically, which requires a time complexity of #plaintexts × 254 and a memory complexity of 280 for recovering one key. In this paper, we improve the χ2-attack to reduce the time complexity. We give the theorem that evaluates the success probability of the χ2-attack on RC6 without using any experimental result. Our key recovery attack recovers keys asymmetrically, which requires a time complexity of #plaintexts × 231 and a memory complexity of 252 for recovering one key. As a result, our key recovery attack works on 16-round RC6 with 192- and 256-bit key and 12-round RC6 with 128-bit key. In the case both of 196- and 256-bit keys, our attack surprisingly reduces the time and memory complexity compared with that of the previous attack. We also demonstrate our theorem on RC6-8/4/8 and make sure of the accuracy by comparing our approximation with the experimental results.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".