Rivest Shamir Adleman (RSA) Hybrid Algorithm System and the deep Blum Blum Shub (BBS) Algorithm Securing E-Absence Database Files
Bibliographic record
Abstract
The E-Attendance System has become an efficient solution in monitoring individual attendance at various institutions. However, new challenges arise regarding data security and privacy in managing E-Attendance database files. Facing potential risks such as hacking and data leaks, data security becomes very important. Therefore, in this study, we propose the implementation of a hybrid system that combines the strengths of the Rivest Shamir Adleman (RSA) Algorithm and the Blum Blum Shub (BBS) Algorithm to improve the security of the E-Absence database file. RSA is a cryptographic algorithm that is widely used for encryption and digital signatures. On the other hand, BBS is a random number generation algorithm that has a strong level of security. The combination of the two in the form of a hybrid system is expected to provide a higher level of security in securing E-Absence data. The aim of this research is to develop a hybrid RSA and BBS system in the context of securing E-Attendance database files. This research outlines the basic concepts of the two algorithms and how they can be integrated. The results of this study are the combination of the Rivers, Shamir, Adleman (RSA) algorithm and the Blum Blum Shub (BBS) algorithm in a hybrid system to increase security in the process of encoding messages
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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 source (direct Gemma or distilled Codex), 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".