EIBC: Enhanced Identity-Based Cryptography, a conceptual design
Bibliographic record
Abstract
Identity-Based Cryptography (IBC) was originally introduced by A. Shamir in 1984 in a signature scheme. IBC was first applied in the encryption and decryption of messages in the Boneh and Franklin model presented in 2001, which forms the basis of our design. In this model, the system has to undergo private key refreshment procedure as part of the key management, which requires multiple control packets that increase the communication overhead. In this paper, we propose Enhanced Identity-Base Cryptography (EIBC), an efficient key management mechanism that minimizes control packets communications. Furthermore, we elucidate how EIBC can be employed in multicast group key managements. We present analysis to show that EIBC simultaneously achieves a high level of system security while handling system key management in an efficiently manner. EIBC can be utilized and implemented in various platforms, e.g., in our efficient authentication and key management schemes for Smart Grid networks.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".