MétaCan
Menu
Back to cohort

Efficient Multiplier and FPGA Implementation for NTRU Prime

2021· article· en· W3211046319 on OpenAlexaff
Huapeng Wu, Xi Gao

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCryptography and Residue Arithmetic
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsNTRUNISTComputer scienceCryptographyElliptic curve cryptographyLattice-based cryptographyPost-quantum cryptographyField-programmable gate arrayAdderPublic-key cryptographyMultiplier (economics)Computer engineeringTheoretical computer scienceArithmeticQuantum cryptographyParallel computingAlgorithmComputer hardwareMathematicsEncryptionComputer securityQuantumCryptosystemQuantum informationTelecommunications

Abstract

fetched live from OpenAlex

As quantum computing age is emerging on horizon, many of the current cryptography standards, e.g., RSA and Elliptic Curve Cryptography, are shown to be compromised under quantum attacks according to Shor's algorithm. A multiple-round competition has been launched by NIST to decide the next generation post-quantum cryptography (PQC) standards since 2017. Entering the final round, NTRU Prime system, proposed in 2016, remains one of a few that have a chance to be part of the future PQC standard. In this work, efficient multiplication architecture is proposed for Streamlined NTRU Prime system. To the best of our knowledge, this work is the first attempt at hardware architecture and implementation of NTRU prime system. Our FPGA implementation results have also shown the proposed multiplier compares favorably to the similar work on the original NTRU system.

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)0.000
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: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.002

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 head0.010
GPT teacher head0.274
Teacher spread0.264 · 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
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

Citations7
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicCryptography and Residue ArithmeticFrench-language works237,207