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Record W2736998317 · doi:10.1109/isvlsi.2017.44

Secured-by-Design FPGA against Early Evaluation

2017· article· en· W2736998317 on OpenAlexaff
Ziyad M. Almohaimeed, Mihai Sima

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCryptographic Implementations and Security
Canadian institutionsUniversity of Victoria
FundersQassim University
KeywordsRobustness (evolution)Field-programmable gate arrayLookup tableComputer scienceSide channel attackCMOSEmbedded systemDigital electronicsLogic synthesisLogic gateComputer hardwareElectronic circuitElectronic engineeringEngineeringElectrical engineeringComputer securityCryptography

Abstract

fetched live from OpenAlex

CMOS power dissipation has multiple components: switching, short-circuit, and static. In order to be robust to power attacks, digital logic should eliminate the relation between processed data and each and every power component. Other sources of side-channel information are glitches and the early evaluation of signals. We improve over our previous work and propose a Look-Up Table (LUT) with increased robustness to early evaluation attacks. The resulting secured-by-design FPGA LUT exhibits quadruple robustness to attacks based on dynamic power, static power, glitches, and early evaluation, whereas its architecture remains in line with commercial FPGAs. The silicon area penalty is light making the disclosed FPGA attractive to cryptoysystems developers.

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 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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.916
Threshold uncertainty score0.753

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.058
GPT teacher head0.334
Teacher spread0.276 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations0
Published2017
Admission routes1
Has abstractyes

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