Attribute-based encryption without key cloning
Bibliographic record
Abstract
In this work, we consider the problem of key cloning in attribute-based encryption schemes. We introduce a new type of attribute-based encryption scheme, called token-based attribute-based encryption (tk-ABE) that provides strong deterrence for key cloning, in the sense that delegation of keys reveals some personal information about the user. It also has the feature that a misbehaving user can be easily revoked. We formalise the security requirements for such a scheme in terms of indistinguishability of the ciphertexts and two new security requirements which we call uncloneability and privacy-preserving. We construct a privacy-preserving uncloneable token-based attribute-based encryption scheme based on Cheung and Newport’s ciphertext-policy attribute-based encryption scheme and prove the scheme satisfies the above three security requirements. We also introduce the notion of non-interactive uncloneable attribute-based encryption in order to remove the online token server in the tk-ABE. We then construct such a new scheme with provable security. It should be pointed out that, although our non-interactive scheme is token-free, the scheme does not have the capability to easily revoke users from the scheme. Hence, both types of schemes have their own merits of existence.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.012 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".