Towards Foundations of Cryptography: Investigation of Perfect Secrecy
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".