Joint channel coding‐cryptography based on random insertions and deletions in quasi‐cyclic‐low‐density parity check codes
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, a new secure channel coding scheme is presented which randomly inserts and deletes bits in a codeword of a quasi‐cyclic‐low‐density parity check (QC‐LDPC) code. It is shown that the key size is smaller than other code‐based cryptosystems based on permutation and scrambling matrices. The positions of the inserted and deleted bits are determined using a secret key. It is shown that the error performance of the resulting code after the insertions and deletions is better than a random low‐density parity check code with similar parameters. An important advantage of this cryptosystem is that even if the QC‐LDPC code is revealed, the system remains secure. Furthermore, the proposed approach using insertions and deletions can be employed with other classes of error correcting codes.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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