The Use of Miller-Rabin in Testing Prime Numbers in the Rsa Algorithm to Secure Files
Bibliographic record
Abstract
The development of digital technology has facilitated data exchange, but it has also increased security risks such as wiretapping and file manipulation. One of the most widely used cryptographic methods for maintaining data confidentiality is the RSA algorithm, whose security depends on large prime numbers as keys. This study utilizes the Miller-Rabin algorithm as a probabilistic primality testing method to ensure the prime numbers used in RSA key generation. The research was conducted by building a system using the Python programming language, which included the process of testing prime numbers with Miller-Rabin, RSA key generation, and document file encryption and decryption. The test results showed that Miller-Rabin was effective in validating large prime numbers and helped speed up the RSA key generation process, so that files could be secured with ciphertext that could be returned to plaintext without data loss. Thus, the implementation of Miller-Rabin in the RSA algorithm has been proven to improve the reliability of digital file security systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".