Rethinking Definitions of Security for Session Key Agreement.
Bibliographic record
Abstract
We consider session key agreement (SKA) protocols operating in a public key infrastructure, with pre-specified peers, that take no session ID as input, and output only a session key. Despite much work on SKA, we argue that there is no good definition of security for this (very natural) protocol syntax. The difficulty is that in this setting the adversary may not be able to tell which processes share a key, and thus which session keys may be revealed without trivializing the security condition. We consider security against adversaries that control all network traffic, can register arbitrary public keys, and can retrieve session keys. We do not attempt to mitigate damage from hardware failures, such as session-state compromise, as we aim to improve our understanding of this simpler setting. We give two natural but substantially different game based definitions of security and prove that they are equivalent. Such proofs are rare for SKA. The bulk of this proof consists of showing that, for secure protocols, only compatible processes can be made to share a key. This property is very natural but surprisingly subtle. For comparison, we give a version of our definition in which processes output session IDs and we give strong theorems relating these two
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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 | 0.027 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.011 | 0.051 |
| Open science | 0.007 | 0.015 |
| Research integrity | 0.007 | 0.027 |
| Insufficient payload (model declined to judge) | 0.006 | 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".