A Novel Approach for Ticket Generation and Validation Using RSA and Keccak Algorithms
Bibliographic record
Abstract
The motivation for this research arises from the challenges faced by railway operators in managing ticketing processes effectively.These challenges highlight the need for a robust web-based application to automate ticket verification and validation, emphasizing the importance of developing a secure and efficient ticket generation and validation system.The proposed solution employs RSA (Rivest-Shamir-Adleman) and Keccak cryptographic algorithms to ensure the security and efficiency of ticket generation and validation.By generating digital signatures and hash functions, the authenticity and integrity of ticket data are maintained.Recipients can utilize the sender's public key and the same hash function to verify the ticket data's authenticity.The system offers several advantages, including security, integrity, authentication, efficiency, and scalability.Future work may involve implementing multi-party computation, developing more efficient algorithms, exploring blockchain technology, and conducting more extensive testing and evaluation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".