Digital Signature Security Analysis By Applying The Elgamal Algorithm And The Idea Method
Bibliographic record
Abstract
The development of an all-digital era, all activities use digital technology. Including signatures, hands are no longer manual now, signatures can be modified digitally. The application of digital signatures can be used to allow for document authenticity issues. The signature combines two methods namely Elgamal Algorithm and IDEA. The Elgamal algorithm is used to encrypt and decrypt signatures. The IDEA algorithm is used to strengthen signatures so that others don't modify them. The signature process begins with generating the public and private keys. The process of generating public keys (p,q,g,y) and private keys. With the signature of a binary document that is given by the sender to the recipient, the authenticity of the contents of the file, the identity of the sender, and the files that have been received by the recipient can be guaranteed. Affixing a digital signature is done by encrypting it with the sender's private key. In this way, checking the authenticity of documents and senders can be done.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".