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Record W3202160978

Faster key compression for isogeny-based cryptosystems

2017· preprint· en· W3202160978 on OpenAlexaff
Gustavo H. M. Zanon, Marcos A. Simplício, Geovandro C. C. F. Pereira, Javad Doliskani, Paulo S. L. M. Barreto

Bibliographic record

VenueUniversity of Washington Tacoma Digital Commons (University of Washington Tacoma) · 2017
Typepreprint
Languageen
FieldComputer Science
TopicCryptography and Residue Arithmetic
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceIsogenyKey (lock)CryptosystemCryptographyElliptic curve cryptographyCompression (physics)Overhead (engineering)Theoretical computer scienceKey encapsulationPublic-key cryptographyComputer engineeringComputer securityKey exchangeMathematicsElliptic curveEncryptionOperating system
DOInot available

Abstract

fetched live from OpenAlex

Supersingular isogeny-based cryptography is one of the more recent families of post-quantum proposals. An interesting feature is the comparatively low bandwidth occupation in key agreement protocols, which stems from the possibility of key compression. However, compression and decompression introduce a significant overhead to the overall processing cost despite recent progress. In this paper we address the main processing bottlenecks involved in key compression and decompression, and suggest substantial improvements for each of them. Some of our techniques may have an independent interest for other, more conventional areas of elliptic curve cryptography as well. IEEE

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.477
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0010.002
Open science0.0100.006
Research integrity0.0010.001
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 head0.021
GPT teacher head0.216
Teacher spread0.195 · 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.

Study designNot applicable
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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