Towards Foundations of Cryptography: Investigation of Perfect Secrecy
Why this work is in the frame
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Bibliographic record
Abstract
In the spirit of Shannon's theory of secrecy systems we analyse several possible natural definitons of the notion of perfect secrecy; these definitions are based on arguments taken from probability theory, information theory, the theory of computational complexity, and the theory of program-size complexity or algorithmic information. It turns out that none of these definitions models the intuitive notion of perfect secrecy completely: Some fail because a cryptographic system with weak keys can be proven to achieve perfect secrecy in their framework; others fail, because a system which, intuitively, achieves perfect secrecy cannot be proven to do so in their framework. To present this analysis we develop a general formal framework in which to express and measure secrecy aspects of information transmission systems. Our analysis leads to a clarification of the intuition which any definition of the notion of perfect secrecy should capture and the conjecture, that such a definition may be i...
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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