MétaCan
Menu
Back to cohort
Record W4404803098 · doi:10.1515/jmc-2023-0012

Revocable policy-based chameleon hash using lattices

2024· article· en· W4404803098 on OpenAlexafffund
Jean Belo Klamti, M. Anwarul Hasan

Bibliographic record

VenueJournal of Mathematical Cryptology · 2024
Typearticle
Languageen
FieldComputer Science
TopicCryptography and Data Security
Canadian institutionsUniversity of WaterlooNational Research Council Canada
FundersUniversity of WaterlooImpact FundSilicon Valley Community Foundation
KeywordsHash functionComputer scienceComputer securityTheoretical computer science

Abstract

fetched live from OpenAlex

Abstract A chameleon hash function is a type of hash function that involves a trapdoor to help find collisions, i.e., it allows the rewriting of a message without modifying the hash. For some applications, it is important to have the feature of revoking the rewriting privilege of the trapdoor holder. In this paper, using lattice-based hard problems that are considered quantum-safe, we first introduce a lattice-based chameleon hash with an ephemeral trapdoor ( CHET ) \left({\mathsf{CHET}}) and then a revocable attribute-based encryption ( RABE {\mathsf{RABE}} ) scheme that is adaptively indistinguishable. We also give security analyses of our schemes and compare our RABE {\mathsf{RABE}} scheme to two relevant schemes proposed recently. Furthermore, we combine our CHET {\mathsf{CHET}} and RABE {\mathsf{RABE}} to design a new revocable policy-based chameleon hash.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0040.009
Open science0.0020.007
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0090.003

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 head0.032
GPT teacher head0.321
Teacher spread0.289 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Mathematical CryptologySame topicCryptography and Data SecurityFrench-language works237,207