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Record W2352592412

A Grou PKey Management Scheme Based on Secret Sharing in WSNs

2010· article· en· W2352592412 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMicrocomputer applications · 2010
Typearticle
Languageen
FieldComputer Science
TopicSecurity in Wireless Sensor Networks
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceSecrecyForward secrecyComputer networkOverhead (engineering)EncryptionKey managementWireless sensor networkSecret sharingSymmetric-key algorithmKey (lock)Node (physics)Computer securityKey distributionDistributed computingPublic-key cryptographyCryptography
DOInot available

Abstract

fetched live from OpenAlex

A grou Pkey management scheme based on secret sharing in wireless sensor network is proposed, which is called GKMSSS. GKMSSS uses LEACH protocol to make the network clustering. Based on the theory of secret sharing and the principle of symmetric key encryption, the grou Pkey components are stored in the various grou Pmembers in a distributed manner. And the key pre-configured, hierarchical key generation, the network sub-clusters, key generation and distribution, key updates, grou Pof new members joining and members of the grou Pexiting are successfully achieved. Through correlation analysis and experiments, it shows that GKMSSS effectively assures the grou Pcommunication forward secrecy, backward secrecy and good anti-prisoner capacity of the node on condition that the storage overhead and communication overhead is in an acceptable situation respectively.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.811
Threshold uncertainty score0.980

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 science0.0020.001
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 head0.006
GPT teacher head0.229
Teacher spread0.223 · 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