Construction of a Hybrid (Hierarchical) Identity-Based Encryption Protocol Secure Against Adaptive Attacks.
Bibliographic record
Abstract
Abstract. The current work considers the problem of obtaining a hierarchical identity-based encryption (HIBE) protocol which is secure against adaptive key extraction and decryption queries. Such a protocol is obtained by modifying an earlier protocol by Chatterjee and Sarkar (which, in turn, is based on a protocol due to Waters) which is secure only against adaptive key extraction queries. The setting is quite general in the sense that random oracles are not used and security is based on the hardness of the decisional bilinear Diffie-Hellman (DBDH) problem. In this setting, the new construction provides the most efficient (H)IBE protocol known till date. The technique for answering decryption queries in the proof is based on earlier work by Boyen, Mei and Waters. Ciphertext validity testing is done indirectly through a symmetric authentication algorithm in a manner similar to the Kurosawa-Desmedt public key encryption protocol. Additionally, we perform symmetric encryption and authentication by a single authenticated encryption algorithm 3.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".