Privacy-aware Attribute-based Encryption with User Accountability.
Bibliographic record
Abstract
As a new public key primitive, attribute-based encryption (ABE) is envisioned to be a promising tool for implementing fine-grained access control. To further address the concern of user access privacy, privacy-aware ABE schemes are being developed to achieve hidden access policy recently. For the purpose of secure access control, there is, however, still one critical functionality missing in the existing ABE schemes, which is user accountability. Currently, no ABE scheme can completely prevent the problem of illegal key sharing among users. In this paper, we tackle this problem by firstly proposing the notion of accountable, anonymous, and ciphertext-policy ABE (CP-A 3 BE, in short) and then giving out a concrete construction. We start by improving the state-of-the-art of anonymous CP-ABE to obtain shorter public parameters and ciphertext length. In the proposed CP-A 3 BE construction, user accountability can be achieved in black-box model by embedding additional user-specific information into the attribute private key issued to that user, while still maintaining hidden access policy. The proposed constructions are provably secure.
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.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".