IBAKE: Identity-Based Authenticated Key Exchange Protocol.
Bibliographic record
Abstract
The past decade has witnessed a surge in exploration of cryptographic concepts based on pairings over Elliptic Curves. In particular, identity-based cryptographic protocols have received a lot of attention, motivated mainly by the desire to eliminate the need for large-scale public key infrastructure. We follow this trend in this work, by introducing a new Identity-Based Authenticated Key Exchange (IBAKE) protocol, and providing its formal proof of security. IBAKE provides mutually-authenticated Key Exchange (AKE) using identities as public credentials. One identity-based AKE subtlety that we address in this work is the resilience to the man-in-the-middle attacks by the Key Management Service. For efficiency, we employ two Elliptic Curves with differing properties. Specifically, we use a combination of a super-singular and non-super-singular curves, where the super-singular curve is used as an identity-based encryption “wrapper” to achieve mutual authentication, and the resulting session key is based on a Diffie-Hellman key exchange in the non-super-singular curve. We provide a detailed proof of security of the resulting protocol with respect to (our own natural adaptation and simplification of) the AKE definitions of Kolesnikov and Rackoff.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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".