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Record W2785873973 · doi:10.1109/pimrc.2017.8292209

SIMON 32/64 and 64/128 block cipher: Study of cross correlation and linear span attack immunity

2017· article· en· W2785873973 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCryptographic Implementations and Security
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBlock cipherComputer scienceCryptographyCipherStream cipherPower analysisRealization (probability)Block (permutation group theory)Correlation attackSpan (engineering)Linear spanTheoretical computer scienceEmbedded systemComputer engineeringAlgorithmMathematicsComputer securityEncryptionEngineeringDiscrete mathematicsStatistics

Abstract

fetched live from OpenAlex

Power and computing limitations hinder the ability of many devices to support stringent security protocols. Smart sensors, RFID tags, and wearable devices are typical examples of such devices. Lightweight cryptography is concerned with the design and implementation of cryptography algorithms in environments with limited computing and power resources. This paper presents a realization of a hardware efficient lightweight cryptography block cipher SIMON in C/C++ (SIMON 32/64 and 64/128). Analysis is performed in order to investigate its input/output cross correlation and among output sets. The proposed block cipher's immunity to linear span attacks is also investigated using the Berlekamp-Massy algorithm. It is concluded that the proposed block cipher is not immune to linear span attacks, as the analysis has shown a linear span for certain components to be less than N/2, with a profile of probability of 1/3 in 1 million iterations.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.622

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.051
GPT teacher head0.369
Teacher spread0.319 · 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