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Record W2073448618 · doi:10.1109/softcom.2014.7039120

RFID encryption scheme featuring pseudorandom numbers and Butterfly seed generation

2014· article· en· W2073448618 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRFID technology advancements
Canadian institutionsDalhousie University
FundersDalhousie University
KeywordsComputer scienceEncryptionPseudorandom number generatorCryptographyComputer securityRadio-frequency identificationSecurity analysisCryptosystemScheme (mathematics)PopularityPseudorandomnessFocus (optics)IdentifierComputer networkAlgorithm

Abstract

fetched live from OpenAlex

The emphasis on security in Radio Frequency Identification (RFID) systems is increasing with each passing day, owing to their corresponding increase in popularity in defence, anti-counterfeiting, logistics and medical applications. However, resource restrictions on RFID tags curtail the use of sophisticated algorithms to achieve better security, and therefore, privacy. Much of the current work has focused on either creating new lightweight cryptosystems specifically for RFID applications or adapting some of the existing techniques for use in RFID applications. Our proposal is a new encryption scheme that uses pseudorandom number generators, a strategic way of updating their seeds and system state identifiers to accomplish security. The focus of our work has been better security, with re-use and simplicity. We evaluate our work using simulation, protocol analysis and security analysis.

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 score0.465
Threshold uncertainty score0.417

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.005
GPT teacher head0.178
Teacher spread0.173 · 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

Quick stats

Citations4
Published2014
Admission routes2
Has abstractyes

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