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Record W2295169600 · doi:10.1145/2808230

Design and Implementation of Warbler Family of Lightweight Pseudorandom Number Generators for Smart Devices

2016· article· en· W2295169600 on OpenAlexaff
Kalikinkar Mandal, Xinxin Fan, Guang Gong

Bibliographic record

VenueACM Transactions on Embedded Computing Systems · 2016
Typearticle
Languageen
FieldComputer Science
TopicCryptographic Implementations and Security
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer sciencePseudorandom number generatorCryptographyRandomnessPseudorandomnessRandom number generationWirelessSecure communicationEmbedded systemComputer networkComputer securityAlgorithmEncryptionTelecommunicationsMathematics

Abstract

fetched live from OpenAlex

With the advent of ubiquitous computing and the Internet of Things (IoT), the security and privacy issues for various smart devices such as radio-frequency identification (RFID) tags and wireless sensor nodes are receiving increased attention from academia and industry. A number of lightweight cryptographic primitives have been proposed to provide security services for resource-constrained smart devices. As one of the core primitives, a cryptographically secure pseudorandom number generator (PRNG) plays an important role for lightweight embedded applications. The most existing PRNGs proposed for smart devices employ true random number generators as a component, which generally incur significant power consumption and gate count in hardware. In this article, we present Warbler family, a new pseudorandom number generator family based on nonlinear feedback shift registers (NLFSRs) with desirable randomness properties. The design of the Warbler family is based on the combination of modified de Bruijn blocks together with a nonlinear feedback Welch-Gong (WG) sequence generator, which enables us to precisely characterize the randomness properties and to flexibly adjust the security level of the resulting PRNG. Some criteria for selecting parameters of the Warbler family are proposed to offer the maximum level of security. Two instances of the Warbler family are also described, which feature two different security levels and are dedicated to EPC C1 Gen2 RFID tags and wireless sensor nodes, respectively. The security analysis shows that the proposed instances not only can pass the cryptographic statistical tests recommended by the EPC C1 Gen2 standard and NIST but also are resistant to the cryptanalytic attacks such as algebraic attacks, cube attacks, time-memory-data tradeoff attacks, Mihaljević et al.’s attacks, and weak internal state and fault injection attacks. Our ASIC implementations using a 65nm CMOS process demonstrate that the proposed two lightweight instances of the Warbler family can achieve good performance in terms of speed and area and provide ideal solutions for securing low-cost smart devices.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.027
GPT teacher head0.302
Teacher spread0.275 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations50
Published2016
Admission routes1
Has abstractyes

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