Efficient Kronecker-based Sparse One-Time Sensing Matrix For Compressive Sensing Cryptosystem
Bibliographic record
Abstract
Compressive Sensing Cryptosystem (CSC) is an approach to simultaneously sense, compress, and encrypt sparse or compressible signals. CSC has attracted attention in the areas of communication and security where efficiency and confidentiality are two key requirements, particularly for small devices or applications over the cloud. Driven by these requirements, we design a measurement matrix that is sparse and efficient, and can be used in CSC applications. We propose a Kronecker-based matrix which is subsequently multiplied by a pseudo-random permutation matrix to construct our sparse one-time sensing (OTS) matrix. The proposed matrix is built using smaller eligible sub-matrices found in the Compressive Sensing (CS) literature. The sparsity of this measurement matrix results in a significant reduction in storage and computational requirements. Furthermore, using ECG signals chosen from the MIT-BIH Arrhythmia database, we demonstrate that the quality of the reconstructed signal does not degrade compared to ordinary CS. We also show that the proposed method is superior to other sparse measurement matrix structures proposed in the literature in terms of the number of linear feedback shift register (LFSR) bits required for their generation. Finally, we analyze security of the proposed scheme and show that the proposed sparse measurement matrix is computationally secure against plain-text and cipher-text only attacks.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".