UNCONDITIONALLY SECURE CONFERENCE KEY DISTRIBUTION: SECURITY NOTIONS, BOUNDS AND CONSTRUCTIONS
Bibliographic record
Abstract
A conference key distribution is a protocol that allows designated subset of users to calculate a shared private key. We consider unconditionally secure conference key distribution systems where the adversary has unlimited computational power, and focuses on a stronger and more realistic adversary model, proposed by Safavi-Naini and Jiang, in which the adversary in addition to corrupting subsets of users and obtaining their private keys, can access the conference keys of a number of uncorrupted conferences. We consider alternative definitions of security with this adversary model and show the relationship between them. An important efficiency parameter for conference key distribution systems is the size of the users' private keys. We derive lower bounds on the size of this key and discuss the results in comparison with the known bounds. We also consider one-round Interactive Conference Key Distribution Systems (ICKDS) in the new adversarial model where the adversary in addition to learning the private keys of the corrupted users and a number of conference keys, has access to the transcripts of some conferences. We show that under this new adversary model, the previously proposed one round protocol of Blundo et. al. is no longer secure and we construct an ICKDS with provable security in the new adversarial model.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.009 | 0.029 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".