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Record W1942829792 · doi:10.1109/fcst.2015.20

Identity-Based Broadcast Encryption Schemes for Open Networks

2015· article· en· W1942829792 on OpenAlexaff
Mingchu Li, Xiaodong Xu, Ruhan Zhuang, Cheng Guo, Xing Tan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCryptography and Data Security
Canadian institutionsYork University
Fundersnot available
KeywordsBroadcast encryptionEncryptionComputer scienceComputer networkScheme (mathematics)Communication sourceProbabilistic encryptionMultiple encryptionAttribute-based encryptionOn-the-fly encryptionIdentity (music)Public-key cryptography40-bit encryptionComputer securityMathematics

Abstract

fetched live from OpenAlex

A Broadcast Encryption (BE) scheme allows a sender to safely transmit messages to a dynamically chosen set of system users via insecure channels. An identity-based encryption scheme is a public key encryption scheme that can take arbitrary strings as public keys. This paper presents an identity-based broadcast encryption scheme (IBBE) for open networks where senders, including the entities outside the system, have the ability to broadcast messages to any subset of the system users but only the target receivers can retrieve the messages. Compared with a recently introduced primitive referred to as contributory broadcast encryption (CBE), our scheme has comparable properties and is more practicable: the cost of our scheme is much lower and the total number of the system users can be efficiently changed.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.004
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.074
GPT teacher head0.333
Teacher spread0.259 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations5
Published2015
Admission routes1
Has abstractyes

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