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Record W4406179441 · doi:10.55041/ijsrem40505

Secured File Sharing Through Quantum Computing

2025· article· en· W4406179441 on OpenAlex
Ms. Gagana G R Nayaka, V. Ananya, Apeksha S Kenchareddy, S P Dhanushree, S Praveen

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
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

VenueINTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · 2025
Typearticle
Languageen
FieldComputer Science
TopicCloud Data Security Solutions
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceFile sharingComputer securityInternet privacyOperating systemThe Internet

Abstract

fetched live from OpenAlex

- In present technology, Quantum computing plays a versatile role in data security and privacy. There are many methods for securing the data one of the methods is cryptography. File encryption and decryption become easier when the AES and RSA algorithms improve security. Also, file sharing through the AWS cloud and downloading from it have become more secure for storing data. Here we can add files into images with the help of Stenography. The comparison among the AES and RSA algorithms of performance speed, key size, and bit size is evaluated. In this paper, we proposed a framework for quantum key-based embedding and de-embedding of files and images where security and privacy are achieved. Access IBM server gives us Quantum processing units(QPUs) that are ibm_sherbrooke, ibm_kyiv, and ibm_brisbane. It requires a total of 127 Qubits and 30K CLOPS. Keywords-AES, RSA, Cryptography, Stenography, AWS Cloud, IBM Server

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)0.003
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.918
Threshold uncertainty score0.642

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.051
GPT teacher head0.353
Teacher spread0.302 · 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