Super Encryption of Rabin Cryptosystem Algorithm and Paillier Cryptosystem Algorithm on Digital Image Security Process
Why this work is in the frame
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Bibliographic record
Abstract
Technological advances have given rise to the need for data protection, especially digital images, which are vulnerable to misuse. This research proposes a super encryption method that combines two cryptographic algorithms, namely Rabin Cryptosystem and Paillier Cryptosystem, to increase digital image security. Rabin's algorithm does not have homomorphism, so it is vulnerable to factorization attacks if the prime numbers used are too small. Meanwhile, the Paillier algorithm has homomorphism properties which allow arithmetic operations to be carried out directly on the ciphertext without decryption. By combining these two algorithms, this research aims to create a stronger and more efficient encryption method, and analyze its performance in terms of computational efficiency and complexity. It is hoped that the research results can improve the security and privacy of digital data, especially in the context of digital images.
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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 | 0.001 | 0.001 |
| 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 it