MétaCan
Menu
Back to cohort
Record W4414977408 · doi:10.3390/telecom6040075

Securing Elliptic Curve Cryptography with Random Permutation of Secret Key

2025· article· en· W4414977408 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTelecom · 2025
Typearticle
Languageen
FieldComputer Science
TopicCryptography and Residue Arithmetic
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsScalar multiplicationElliptic curve cryptographyElliptic curveScalar (mathematics)Elliptic Curve Digital Signature AlgorithmCryptographyElliptic curve point multiplicationPublic-key cryptography

Abstract

fetched live from OpenAlex

Scalar multiplication is the basis of the widespread elliptic curve public key cryptography. Standard scalar multiplication is vulnerable to side-channel attacks that are able to infer the secret bit values by observing the power or delay traces. This work utilizes the arithmetic properties of scalar multiplication to propose two scalar multiplication algorithms to insulate ECC implementations from side-channel attacks. The two proposed designs rely on randomly permuting the ordering and storage locations of the different scalar multiplication values 2iG as well as the corresponding secret key bits ki. Statistical analysis and Python 3.9.13implementations confirm the validity of the two algorithms. Numerical results confirm that both designs produce the same results as the standard right-to-left scalar multiplication algorithm. Welch’s t-test as well as numerical simulations confirm the immunity of our proposed protocols to side-channel attacks.

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.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.609
Threshold uncertainty score0.423

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.003
GPT teacher head0.209
Teacher spread0.205 · 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