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Record W2170943789 · doi:10.1109/ccece.2004.1345228

An improved FPGA implementation of a hyperelliptic cryptosystem coprocessor

2004· article· en· W2170943789 on OpenAlexaff
Ghaidaa Hadi Salih Elias, Lo Sing Cheng, Ali Miri, Tet Yeap

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCryptography and Residue Arithmetic
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCryptosystemCoprocessorDiscrete logarithmField-programmable gate arrayComputer scienceFinite fieldHyperelliptic curveScalar multiplicationPolynomialArithmeticHyperelliptic curve cryptographyMultiplication (music)Timing attackPolynomial basisCryptographyField (mathematics)Parallel computingMathematicsAlgorithmPublic-key cryptographyElliptic curve cryptographyDiscrete mathematicsEncryptionElliptic curveSide channel attackComputer hardwarePure mathematicsCombinatorics

Abstract

fetched live from OpenAlex

A mechanism for security that is becoming more popular is the hyperelliptic curve cryptosystem (HECC). HECC derives its security on the difficulties in solving the discrete logarithm problem, where its major advantage is that it offers much shorter key lengths for the same level of security as RSA and ECC. This paper outlines an FPGA implementation of a HECC coprocessor and introduces an efficient FPGA implementation of a finite field multiplier and polynomial addition module used in a HECC. The HECC implementation is based on Cantor's algorithm for performing point addition and point doubling, which depends on the implementation of efficient polynomial arithmetic blocks that in turn depends on efficient finite field arithmetic blocks. The suggested field multiplication block is one of the most important blocks and is efficient in area, speed, and number of clock cycles to complete the operation.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.618
Threshold uncertainty score0.302

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 head0.008
GPT teacher head0.265
Teacher spread0.257 · 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 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

Citations0
Published2004
Admission routes1
Has abstractyes

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