MétaCan
Menu
Back to cohort
Record W2901118812 · doi:10.1109/access.2018.2881130

Differential Cryptanalysis of Round-Reduced LEA

2018· article· en· W2901118812 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Access · 2018
Typearticle
Languageen
FieldComputer Science
TopicCryptographic Implementations and Security
Canadian institutionsBrandon University
Fundersnot available
KeywordsImpossible differential cryptanalysisLinear cryptanalysisDifferential cryptanalysisCryptanalysisHigher-order differential cryptanalysisComputer scienceDifferential (mechanical device)Boomerang attackComputer securityCryptographyPhysics

Abstract

fetched live from OpenAlex

In this paper, we focus on the differential cryptanalysis dedicated to a particular class of cryptographic algorithms, namely ARX ciphers. We propose a new algorithm inspired by the Nested Monte-Carlo Search algorithm to find a differential path in ARX ciphers. We apply our algorithm to a round reduced variant of the block cipher LEA. For small blocks of ARX ciphers, our algorithm works perfectly and in an extremely concise time. Taking into account that our algorithm takes longer for bigger blocks, we use the concept of a partial difference distribution table (pDDT) in our algorithm. This methodology reduced the search space of the algorithm by using only those differentials whose probabilities are greater than or equal to a pre-defined threshold. Using this concept, we removed many differentials which are not valid or whose probabilities are very low. This led to a decreased time of finding a differential path by our nested algorithm due to a smaller search space. This partial difference distribution table also made our nested algorithm suitable for bigger block size ARX ciphers. In previous works, finding long differential characteristics has been shown to be a problem of a harder nature where algorithms have been shown to take many hours or days to find differential characteristics in ARX ciphers. In this paper, our algorithm finds the differential characteristics in just a few minutes with a very simple framework. We report the differential path for up to nine rounds in LEA. To construct differential characteristics for a large number of rounds, we use techniques to divide long characteristics into short ones, by constructing a large characteristic from two short characteristics. Furthermore, instead of starting from the first round as most algorithms do, we start from the middle and run experiments in the forward as well as in the reverse direction. Using this method, we improved our results and report the differential path for up to 12 rounds and with the given path we attacked 14 rounds of cipher. Overall, it is clear to see that the best property of our algorithm is that it has the potential to provide state-of-the-art results but within a simpler framework as well as in less time than previous attempts. Our algorithm provides a reusable framework for future avenues of research, as it could be applied to other ARX ciphers with the potential for interesting and efficient resultss.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.445
Threshold uncertainty score0.352

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.352
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it