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Record W4415360562 · doi:10.59934/jaiea.v5i1.1682

Implementation of Number Theoretic Transform Unit (NTRU) Cryptography to Secure Text Files

2025· article· W4415360562 on OpenAlex
Arta Naila Fanaya, Rahmadani Rahmadani, Marto Sihombing

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

VenueJournal of Artificial Intelligence and Engineering Applications (JAIEA) · 2025
Typearticle
Language
FieldComputer Science
TopicChaos-based Image/Signal Encryption
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsNTRUCiphertextEncryptionCryptographyPublic-key cryptographyKey (lock)Confidentiality

Abstract

fetched live from OpenAlex

This research aims to develop a text file security system using the NTRU cryptographic algorithm. This process begins with understanding the basic principles of NTRU, which uses a polynomial-based mathematical structure to generate key pairs. The public key is used to encrypt text files, while the private key is used to decrypt them. The research stages include generating public-private keys, followed by the process of encrypting text files into ciphertext and decrypting to restore the files to their original form. The result of this research is a system capable of effectively securing text files using NTRU encryption. This system is expected to contribute to improving digital information security, especially in protecting the confidentiality of text files. This research report will provide insights and detailed steps regarding the implementation of NTRU. This research is expected to be a guide for developers and researchers in implementing NTRU to strengthen broader data security.

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 categoriesMeta-epidemiology (narrow)
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 score0.919
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.015
GPT teacher head0.301
Teacher spread0.286 · 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