Accelerating Secure and Verifiable Data Deletion in Cloud Storage via SGX and Blockchain
Bibliographic record
Abstract
Secure data deletion enables data owners to have full control over the erasure of their data stored on local or cloud data centers, and it is essential for preventing data leakage, especially in cloud storage. However, traditional data deletion methods based on unlinking, overwriting, and cryptographic key management are either ineffective in cloud storage or rely on impractical assumptions. In this paper, we introduce SevDel, a secure and verifiable data deletion scheme that utilizes zero-knowledge proofs to achieve verification of the encryption of outsourced data without retrieving the ciphertexts. Meanwhile, the deletion of encryption keys is guaranteed based on Intel SGX. SevDel implements secure interfaces for performing data encryption and decryption in secure cloud storage. It also utilizes smart contracts to enforce the operations of the cloud service provider, ensuring compliance with service level agreements with data owners and imposing penalties on the service provider for disclosing cloud data on its servers. Evaluation using real-world workloads demonstrates that SevDel efficiently achieves data deletion verification and maintains high bandwidth savings.
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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 | 0.005 | 0.010 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".