Accelerating Secure and Verifiable Data Deletion in Cloud Storage via SGX and Blockchain
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
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.
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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