Bibliographic record
Abstract
Abstract—Identity-based encryption (IBE) has shown to be a useful cryptographic scheme enabling secure yet flexible role-based access control. We propose a new variant of IBE named as exclusion-intersection encryption: during encryption, the sender can specify the targeted groups that are legitimate and interested in reading the documents; there exists a trusted key generation centre generating the intersection private decryption keys on request. This special private key can only be used to decrypt the ciphertext which is of all the specified groups ’ interests, its holders are excluded from decrypting when the documents are not targeted to all these groups (e.g., the ciphertext of only a single group’s interest). While recent advances in cryptographic techniques (e.g., attribute-based encryption or wicked IBE) can support a more general access control policy, the private key size may be as long as the number of attributes or identifiers that can be specified in a ciphertext, which is undesirable, especially when each user may receive a number of such keys for different decryption power. One of the applications of our notion is to support an ad-hoc joint project of two or more groups which needs extra helpers that are not from any particular group. Index Terms—access control, applied cryptography, compact private key, data confidentiality, identity-based encryption, pair-ings I.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".