Development of Dynamic Key Based on Pseudo-Random Algorithm in Vigenere Cipher for Hybrid Vigenere-ElGamal Encryption to Secure Documen Data
Bibliographic record
Abstract
The security of digital documents has become increasingly critical in line with the growing threats to the confidentiality and integrity of information. This study aims to develop a hybrid cryptographic system that combines the Vigenère Cipher with dynamic keys generated through the Linear Congruential Generator (LCG) and the ElGamal algorithm. The application of dynamic keys in the Vigenère Cipher enhances randomness and reduces predictability, while the ElGamal algorithm strengthens protection through public-key cryptography. The system was designed and implemented as a desktop application using Visual Basic .NET Framework 4.0, supporting the encryption and decryption of PDF, DOCX, and XLSX files. Each data byte is processed according to the algorithm sequence stored in a hybrid file format, ensuring accurate decryption results. Experimental testing demonstrated that the system can successfully restore encrypted documents to their original form without data loss, thereby maintaining document integrity. However, the ciphertext size increased to nearly three times that of the original file due to the ElGamal mechanism, which produces two values for each byte. This study concludes that the hybrid Vigenère–ElGamal method with LCG-based dynamic keys can effectively enhance the security of digital document encryption, although it results in a larger ciphertext size.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".