A Proxy-Based and Collusion Resistant Multi-Authority Revocable CPABE Framework with Efficient User and Attribute-Level Revocation (PCMR-CPABE)
Bibliographic record
Abstract
The presence of multiple authorities in multi-authority ciphertext policy attribute based encryption (CPABE) schemes hinders an adversary's ability to compromise security.As each authority is responsible to provide secret keys to the users, thus enforcement of finegrained access control should be carefully designed to ensure data confidentiality.The current study critically reviews the methodologies employed to address user-level and attribute-level revocation in the existing studies.The study has focused on the revocation methodology of those CPABE schemes that are implemented using bilinear pairing cryptography for the encryption and Linear Secret Sharing Scheme (LSSS) for the access structure.It has been observed that the approaches implemented in the existing schemes are computationally expensive and are vulnerable to collusion attacks caused by the cloud and revoked users.Thus, an efficient proxy-based and collusion resistant multi-authority revocable CPABE framework (PCMR-CPABE) is proposed in the current study.The proposed framework is decentralized, dynamic, scalable, and ensures forward/backward secrecy.Additionally, the proposed framework is computationally efficient and is practical to implement as it does not require secret key or group secret key and ciphertext update to address revocation.Furthermore, the incorporation of time and identity-based components allows the proposed framework to resist collusion attacks efficiently.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".