Proof of Storage for Video Deduplication in the Cloud
Bibliographic record
Abstract
With the advent of cloud computing and its technologies, including data deduplication, more freedom are offered to the users in terms of cloud storage, processing power and efficiency, and data accessibility. The digital data has attained unexceptional growth due to the common use of internet and digital devices giving rise to Big Data problem world wise. These huge volumes of data need some practical platforms for the storage, processing and availability and cloud technology offers all the potentials to fulfil these requirements. Data deduplication is referred to as a strategy offered to cloud storage providers (CSPs) to eliminate the duplicate data and keep only a single unique copy of it for storage space saving purpose to condense Big Data issues. But these benefits also come with data security and privacy issues associated with the cloud technology since the data owner looses the physical control of its data once uploaded in the cloud storage and the CSP gains a complete ownership of the data. In this paper, assuming that the CSP is semi-honest (i.e. Honest but curious and cannot be completely trusted), a proof of retrievability (POR) and a proof of ownership (POW) are proposed for video deduplication in cloud storage environments. The POW protocol is meant to be used by the CSP to authenticate the true owner of the data video before releasing it whereas the POR protocol is meant to allow the user to check that his/her data video stored in the cloud is secured against any malicious user or the semi-honest CSP. These schemes are proposed as complement to our earlier proposed scheme for securing the video deduplication in the cloud storage through the H.264 compression algorithm. Some experimental results are provided, showing the effectiveness of our proposed POR and POW protocols.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".