Securing Speech Noise Reduction in Outsourced Environment
Bibliographic record
Abstract
Cloud data centers (CDCs) are becoming a cost-effective method for processing and storage of multimedia data including images, video, and audio. Since CDCs are physically located in different jurisdictions, and are managed by external parties, data security is a growing concern. Data encryption at CDCs is commonly practiced to improve data security. However, to process the data at CDCs, data must often be decrypted, which raises issues in security. Thus, there is a growing demand for data processing techniques in encrypted domain in such an outsourced environment. In this article, we analyze encrypted domain speech content processing techniques for noise reduction. Noise contaminates speech during transmission or during the acquisition process by recording. As a result, the quality of the speech content is degraded. We apply Shamir’s secret sharing as the cryptosystem to encrypt speech data before uploading it to a CDC. We then propose finite impulse response digital filters to reduce white and wind noise in the speech in the encrypted domain. We prove that our proposed schemes meet the security requirements of efficiency, accuracy, and checkability for both semi-honest and malicious adversarial models. Experimental results show that our proposed filtering techniques for speech noise reduction in the encrypted domain produce similar results when compared to plaintext domain processing.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".