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Record W2058559153 · doi:10.1049/iet-ifs.2011.0033

On the sliding property of SNOW 3G and SNOW 2.0

2011· article· en· W2058559153 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

VenueIET Information Security · 2011
Typearticle
Languageen
FieldComputer Science
TopicCryptographic Implementations and Security
Canadian institutionsConcordia University
Fundersnot available
KeywordsStream cipherSnowKey (lock)Computer scienceCipherProperty (philosophy)Mathematical proofSet (abstract data type)Theoretical computer scienceCryptographyAlgorithmMathematicsComputer securityMeteorologyProgramming languageGeography

Abstract

fetched live from OpenAlex

SNOW 3G is a stream cipher chosen by the 3rd Generation Partnership Project (3GPP) as a crypto-primitive to substitute KASUMI in case its security is compromised. SNOW 2.0 is one of the stream ciphers chosen for the ISO/IEC standard IS 18033-4. In this study, the authors show that the initialisation procedure of the two ciphers admits a sliding property, resulting in several sets of related-key pairs. In case of SNOW 3G, a set of 232 related-key pairs is presented, whereas in the case of SNOW 2.0, several such sets are found, out of which the largest are of size 264 and 2192 for the 128-bit and 256-bit variant of the cipher, respectively. In addition to allowing related-key recovery attacks against SNOW 2.0 with 256-bit keys, the presented properties reveal non-random behaviour that yields related-key distinguishers and also questions the validity of the security proofs of protocols that are based on the assumption that SNOW 3G and SNOW 2.0 behave like perfect random functions of the key–IV.

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.341
Threshold uncertainty score0.189

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.035
GPT teacher head0.242
Teacher spread0.207 · 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