Bibliographic record
Abstract
The proposed work is to provide security for audio data stored in cloud data centers and online song repositories such as hungama.com, iTunes, where huge number of songs is stored for listening and downloading. When polynomial secret sharing method is applied on secret audio, audio shares are generated each with the size of secret audio. Secret is derived only when a valid subset of members in the group are compromised. The purpose of the proposed work is to reduce the dimensions of shares. This can be achieved by having the amplitude values of the secret audio as the coefficients of the polynomial instead of random values in polynomial based secret sharing scheme. But having amplitude values as coefficients does not generate meaningless shares and reveals information about the secret. In this paper, two methods are proposed for generating meaningless audio shares such that the dimensions of each share are lesser than the dimensions of confidential audio. It gives dealer an advantage to securely store and distribute the shares by hiding the shares into other media. In the first method, secret audio is encrypted and the values of these encrypted audio are taken as the coefficients of the polynomial to generate the shares. Second method is implemented to reduce the burden of encryption and decryption. In this approach, for share construction the leading term in the polynomial will have random values as coefficient and for all other terms the coefficient are amplitude values of the secret audio. In both methods, shares generated are of reduced dimensions and are meaningless. Security of shares is enhanced by providing integrity mechanism to shares and steganography effect. A novel block cipher is implemented as compression function in the hash algorithm to generate checksum which is used for verifying integrity of shares.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.010 |
| Open science | 0.001 | 0.001 |
| 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".