Identity-Based Provable Data Possession From RSA Assumption for Secure Cloud Storage
Bibliographic record
Abstract
As cloud storage services have become popular nowadays, the integrity of outsourced data stored at untrusted servers received increased attention. Provable data possession (PDP) provides an effective and efficient solution for cloud data integrity by asking the cloud server to prove that the stored data are not tampered with or maliciously discarded without returning the actual data to users. In this article, we propose an efficient identity-based privacy-preserving provable data possession scheme (ID-P$^3$DP) based on the RSA assumption for secure cloud storage. In ID-P$^3$DP, a cloud user takes the outsourcing file and a global parameter in a time period as inputs to generate identity-based homomorphic authenticators, and any third-party auditor (TPA) can check the integrity of the outsourced file by verifying the validity of homomorphic authenticators. The distinguished feature of ID-P$^3$DP is to support the aggregation of identity-based homomorphic authenticators generated by different users under the RSA assumption, which is an open problem in provable data possession. Specifically, we transfer the identity-based homomorphic authenticators generated in distinct time periods into those with the same period parameter, and the cloud can compress the homomorphic authenticators of different users to generate a data possession proof for integrity verification. Besides, by exploiting zero-knowledge proof, the leakage of outsourced data to TPA can be prevented. The soundness of ID-P$^3$DP is proved based on the RSA assumption, and the privacy against TPA is perfectly preserved. Finally, we demonstrate ID-P$^3$DP is more efficient on integrity verification than the existing BLS-based schemes, and cross-user aggregate verification can significantly reduce computational and communication overhead for TPA.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".