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Record W4320925766 · doi:10.18280/rces.090404

Security Issues in Cloud Computing: A Study

2022· article· en· W4320925766 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

VenueReview of Computer Engineering Studies · 2022
Typearticle
Languageen
FieldComputer Science
TopicCloud Data Security Solutions
Canadian institutionsnot available
Fundersnot available
KeywordsCloud computingCloud computing securityComputer scienceComputer securityThe InternetServerOpenness to experienceUtility computingArchitectureComputer security modelWorld Wide Web

Abstract

fetched live from OpenAlex

Cloud computing is the study of using remote servers which are hosted on the internet to deliver on-demand computing resources on pay-for-use basis rather than a local server. It provides online services via third party which own the infrastructure. Due to the openness of cloud computing systems, customers have concerns about security point of view in the adoption of the services which they provide. The purpose of this paper is to give a detail regarding to the data security and privacy safety issues in cloud computing systems. A security architecture discussed in this paper to provide security measures. In this architecture it can be seen that every level provides security measures in different ways. Then this paper will also discuss some of the current available solutions. According to the findings of this research, in the designing phase of the applications which are based on cloud, there should be taken security measures to increase the trust level of customers’ and organizations.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.882
Threshold uncertainty score0.913

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.003
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 head0.024
GPT teacher head0.312
Teacher spread0.288 · 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