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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 OpenAlexaff
Aleksandar Kircanski, Amr Youssef

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0020.004
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations31
Published2011
Admission routes1
Has abstractyes

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