Secure Textual Data Deduplication Scheme Based on Data Encoding and Compression
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As the need for storage has grown exponentially in recent years, cloud storage has been providing a solution to this need by providing users expanded capacity and access. Providing adequate security and privacy, and lowering storage costs are some of the key challenges facing this solution. A common practice used by cloud service providers (CSPs)-data deduplication - identifies identical copies of users' data, and removing all, but one copy to lower required storage overhead. However, this can result in serious privacy concerns. In this paper, we formulate a new secure deduplication scheme for textual data. Our proposed method uses data encoding and compression techniques that not only result in reduce storage space required, but also in saving in required transmission bandwidth. The security of the data against the semi-honest CSP and malicious users is ensured by using Burrows Wheel Transform encoding scheme. The encoded data is further compressed to gain effective savings in terms of storage and reduced size of the data. Data encoding and data compression techniques are combined together to realize secure and efficient data deduplication. Through our scheme, the CSP will not only achieve huge storage space savings through data compression and data deduplication, but can also provide the users a satisfactory level of security for their data in the cloud.
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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 | 0.004 | 0.004 |
| 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