CRT based somewhat homomorphic encryption over the integers
Bibliographic record
Abstract
Over the last decade, the demand for privacy and data confidentiality in communication and storage processes have increased exponentially. Cryptography can be the solution for this demand. However, the critical issue occurs when there is a need for computing publicly on sensitive information or delegating computation to untrusted machines. This must be done in such a way that preserves the information privacy and accessibility. For this reason, we need an encryption algorithm that allows computation on information without revealing details about them. In 1978 Rivest, Adleman and Dertouzos [1] raised a crucial question: can we use a special privacy homomorphism to encrypt the data and do an unlimited computations on it while it remains encrypted without the necessity of decrypting it? Researchers made extensive efforts to achieve such encryption algorithm. In this paper, we introduce the implementation of the CRT-based somewhat homomorphic encryption over the integers scheme. The main goal is to provide a proof of concept of this new and promising encryption algorithm.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".