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BLS12-381 Pairing Implementation with RAM Footprint Smaller than 4KB

2022· article· en· W4312305644 on OpenAlexfundno aff
Riku Anzai, Junichi Sakamoto, Naoki Yoshida, Tsutomu Matsumoto

Bibliographic record

Venue2022 37th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC) · 2022
Typearticle
Languageen
FieldComputer Science
TopicCryptography and Data Security
Canadian institutionsnot available
FundersSwine Innovation Porc
KeywordsComputer scienceEncryptionCryptographyPairingElliptic curve cryptographyArduinoSignature (topology)Embedded systemComputer networkTheoretical computer scienceComputer securityPublic-key cryptographyMathematics

Abstract

fetched live from OpenAlex

Advanced cryptographic technologies such as identity-based encryption, broadcast encryption, and aggregate signature are expected to be utilized for smart security management of IoT (Internet of Things) systems. Most advanced cryptography is based on complicated algebraic operations, namely bilinear pairings defined over elliptic curve groups. BLS12-381 pairing is the most promising one that satisfies a sufficient security level, and it is of great interest whether this pairing can be implemented on resource-restricted IoT end nodes. This paper derives Techniques to implement BLS12-381 pairing with less than 4 KB of RAM and evaluates its performance on two resource-restricted microcontrollers, Arduino Nano Every and Arduino Nano 33 IoT.

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)
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 score0.944
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0050.004
Research integrity0.0000.001
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 head0.060
GPT teacher head0.301
Teacher spread0.241 · 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 designTheoretical or conceptual
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

Citations1
Published2022
Admission routes1
Has abstractyes

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