Privacy-Preserving Data Publish-Subscribe Service on Cloud-based Platforms.
Bibliographic record
Abstract
Abstract—Data publish-subscribe service is an effective ap-proach to share and filter data. Due to the huge volume and veloc-ity of data generated daily, cloud systems are inevitably becoming the platform for data publication and subscription. However, the privacy becomes a challenging issue as the cloud server cannot be fully trusted by both data publishers and data subscribers. In this paper, we propose a privacy-preserving data publish-subscribe service for cloud-based platforms. Specifically, we first formulate the problem of privacy-preserving data publish-subscribe service by refining its security requirements on cloud-based platforms. Then, we propose a bi-policy attribute-based encryption (BP-ABE) scheme as the underlying technique that enables the encryptor to define access policies and the decryptor to define filtering policies. Based on BP-ABE, we also propose a Privacy-preserving Data Publish-Subscribe (PDPS) scheme on cloud-based platforms, which enables the cloud server to evaluate both subscription policy and access policy in a privacy-preserving way. The security analysis and performance evaluation show that the PDPS scheme is secure in standard model and efficient in practice.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.025 | 0.051 |
| Research integrity | 0.001 | 0.004 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".