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Record W4391075432 · doi:10.23940/ijpe.24.01.p5.3239

SDS-IAM: Secure Data Storage with Identity and Access Management in Blockchain

2024· article· en· W4391075432 on OpenAlex
Sikarwar Sahil, N. Jeyanthi, R. Thandeeswaran, M. R. Abd Hamid

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Performability Engineering · 2024
Typearticle
Languageen
FieldComputer Science
TopicBlockchain Technology Applications and Security
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsBlockchainIdentity managementComputer scienceIdentity (music)Computer securityChemistryDatabaseAccess controlPhysics

Abstract

fetched live from OpenAlex

Identity and Access management (IAM) [1] plays an important role when it comes to background verification.It is a great way to know people you are working with, whether it is a professional front or some local business.Identity theft and secure document exchange are major issues with the current scenario and blockchain offers to be a great solution.The introduction of the public key, private key, transaction verification and foot printing will play a significant role in securing IAM.The idea is to store user documents and other critical information inside the block chain.All the verification is given based on the user's consensus to the particular request which will trigger further functionalities, responsible for secure data exchange.According to the property of blockchain, the chain will contain the history of each transaction that keeps track of every user-company activity that will prevent any action which is against the will of both parties.

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.872
Threshold uncertainty score0.371

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.000
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 head0.017
GPT teacher head0.293
Teacher spread0.276 · 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