A Lightweight Symmetric Encryption Framework Using Homogeneous and Non-Homogeneous Caterpillar Graphs
Bibliographic record
Abstract
In today’s interconnected world, ensuring secure and efficient communication is of critical importance. Traditional cryptographic techniques often encounter challenges such as high computational costs and vulnerabilities to emerging attack strategies. This study proposes a novel encryption framework that leverages the structural properties of homogeneous and non-homogeneous caterpillar graphs to enhance the processes of encrypting and decrypting textual information. Plaintext characters are first mapped to numerical values based on their positions in the English alphabet, after which number-theoretic operations are applied to generate ciphertext. The encrypted values are embedded within caterpillar graph structures, where vertex assignments and coloring methods introduce additional layers of complexity. This integration not only increases resistance to brute-force and quantum-based attacks but also improves visualization and segmentation of encrypted blocks. Furthermore, efficient graph traversal algorithms are incorporated to optimize computational performance. The proposed framework significantly strengthens cryptographic security by combining graph theory, number theory, and coloring techniques, offering a scalable solution to modern cybersecurity challenges.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".