Revocable and Privacy-Preserving Bilateral Access Control for Cloud Data Sharing
Bibliographic record
Abstract
In this paper, we propose a revocable and privacy-preserving bilateral access control scheme (named PriBAC) for general cloud data sharing (i.e., end-cloud-based data sharing). PriBAC ensures that preference matching is successful only when both parties’ preferences are satisfied simultaneously. Otherwise, nothing is leaked beyond whether the preference matching occurs. There are three challenges in designing PriBAC. The first challenge is protecting matching information, i.e., concealing two preference matching processes, in a single cloud server. The second challenge is protecting preference content while preventing receivers from receiving much useless information. The third challenge is how to integrate efficient user revocation mechanisms into bilateral access control to handle frequent user revocation cases in practical cloud data sharing applications. To address the above challenges, the punchline in PriBAC is to leverage Newton’s interpolation formula-based secret sharing to enrich the matchmaking encryption technique for constructing a privacy-preserving preference matching mechanism. To achieve efficient user revocation, we integrate a unique symbol into each user’s keys and efficiently revoke users by invaliding the corresponding keys. Security analysis proves that PriBAC can resist the chosen-ciphertext attack and preserves preference privacy and matching privacy. Experiments show that PriBAC achieves approximately$3\times $user performance improvement compared with current state-of-the-art related schemes.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
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 | 0.003 | 0.006 |
| 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 | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".