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Warbler: A Lightweight Pseudorandom Number Generator for EPC C1 Gen2 Passive RFID Tags

2013· article· en· W2467634029 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

VenueInternational Journal of RFID Security and Cryptography · 2013
Typearticle
Languageen
FieldComputer Science
TopicCryptographic Implementations and Security
Canadian institutionsUniversity of Waterloo
FundersDivision of Chemistry
KeywordsPseudorandom number generatorComputer scienceRandom number generationNISTRandomnessCryptanalysisElliptic Curve Digital Signature AlgorithmGenerator (circuit theory)PseudorandomnessAlgorithmCryptographyEmbedded systemComputer networkPublic-key cryptographyPower (physics)MathematicsElliptic curve cryptography

Abstract

fetched live from OpenAlex

A pseudorandom number generator is an important component for implementing security functionalities on RFID tags. Most previous proposals focus on true random number generators that are usually inefficient for low-cost tags in terms of power consumption, area, and throughput. In this contribution, we propose a lightweight pseudorandom number generator (PRNG) for EPC Class-1 Generation-2 (EPC C1 Gen2) RFID tags. The proposed PRNG fully exploits nonlinear feedback shift registers and provides 16-bit random numbers that are required in the tag identification protocol of the EPC C1 Gen2 standard. The generated sequences are able to pass the EPC C1 Gen2 standard's statistical tests as well as the NIST randomness test suite. Moreover, a detailed cryptanalysis shows that the proposed PRNG is resistant to the most common attacks such as algebraic attacks, cube attacks, and time-memorydata tradeoff attacks. In particular, the proposed PRNG can be implemented on low-cost Xilinx Spartan-3 FPGA devices with 46 slices.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.250
Threshold uncertainty score0.902

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.007
GPT teacher head0.258
Teacher spread0.251 · 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