UNCONDITIONALLY SECURE CONFERENCE KEY DISTRIBUTION: SECURITY NOTIONS, BOUNDS AND CONSTRUCTIONS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 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 it