A Secure Video Deduplication Scheme in Cloud Storage Environments Using H.264 Compression
Why this work is in the frame
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Bibliographic record
Abstract
Due to the rapidly increasing amounts of digital data produced worldwide, multi-user cloud storage systems are becoming very popular and Internet users are approaching cloud storage providers (CSPs) to upload their data in the clouds. Among these data, digital videos are fairly huge in terms of storage cost and size, and techniques that can help reducing the cloud storage cost and size are always desired. This paper argues that data reduplication can ease the problem of BigData storage by identifying and removing the duplicate copies from the cloud storages. Although reduplication maximizes the storage space and minimizes the storage costs, it comes with serious issues of data privacy and security. Though the users desire to save some cost by allowing the CSP to deduplicate their data, they do not want the CSP to wane the privacy of their data. In this paper, a scheme is proposed that achieves a secure video reduplication in cloud storage environments. Its design consists of embedding a partial convergent encryption along with a unique signature generation scheme into a H.264 video compression scheme. The partial convergent encryption scheme is meant to ensure that the proposed scheme is secured against a semi-honest CSP, the unique signature generation scheme is meant to enable a classification of the encrypted compressed video data in such a way that the reduplication can be efficiently performed on them. Experimental results and security analysis are provided to validate the stated goals.
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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 | 0.000 | 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 | 0.001 |
| 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