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Energy Efficiency Analysis of Elliptic Curve Based Cryptosystems

2018· article· en· W2890105645 on OpenAlexafffund
Tanushree Banerjee, M.A. Hasan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCryptography and Residue Arithmetic
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElliptic curve cryptographyCurve25519Energy consumptionSupersingular elliptic curveComputer scienceIsogenyTripling-oriented Doche–Icart–Kohel curveElliptic Curve Digital Signature AlgorithmElliptic curveMathematicsAlgorithmParallel computingPublic-key cryptographyEncryptionPure mathematicsComputer networkEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Energy consumption is an important factor for any cryptoscheme, implemented on devices having limited energy resource. In this paper, we analyze the energy consumption during the Diffie-Hellman key exchange implemented on both supersingular and ordinary elliptic curves. The former protocol is Supersingular Isogeny based Diffie-Hellman (SIDH) and the latter is Elliptic Curve based Diffie-Hellman (ECDH) respectively. Implementations are executed on 64 bit Intel Skylake processor. The energy consumption is analysed for the classical bit security levels of 128 and 192. In this paper, a detailed comparison of power and energy consumption by elliptic curve point addition and doubling operations is presented for affine and standard projective coordinates. Projective coordinates based elliptic curve operations are found to be around 50 to 60 times more energy efficient. We then analyze SIDH and ECDH, implemented using projective coordinates. Our results show that, SIDH consumes around 37 to 47 times more energy in comparison to ECDH for the above mentioned bit security levels.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.965
Threshold uncertainty score0.332

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.009
GPT teacher head0.228
Teacher spread0.218 · 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 designSimulation or modeling
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

Citations6
Published2018
Admission routes2
Has abstractyes

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