A Data Integrity Verification Scheme with Secure Deduplication in Smart Grid Cloud Storage
Why this work is in the frame
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Bibliographic record
Abstract
In order to solve the problem of data deduplication and data integrity in smart grid cloud storage, a data integrity verification scheme which can support security deduplication is proposed in the context of smart grid cloud storage. This scheme refers to an efficient and safe deduplication method based on the bloom filter, realizes the quick verification of the user's hash value and initialization value. In addition, the secure deduplication method is combined with S—PDP integrity verification mechanism, data segmentation and random sampling strategy, to realize the security deduplication and integrity verification of smart grid clients. By using erasure correction code, users can repair the damaged data. The analysis results show that this scheme can effectively reduce the computing cost and communication cost while ensuring that the cloud storage data of smart grid can be secure deduplicated and integrity verified.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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