Composing Kerberos and Multimedia Internet KEYing (MIKEY) for AuthenticatedTransport of Group Keys
Bibliographic record
Abstract
We motivate and present two designs for the composition of the authentication protocol, Kerberos, and the key transport protocol, Multimedia Internet KEYing (MIKEY) for authenticated transport of cryptographic keys for secure group-communication in enterprise and public-safety settings. A technical challenge, and our main contribution, is the analysis of the security of the composition. Towards this, we design our compositions to have intuitive appeal and thereby less prone to security vulnerabilities. We then employ protocol composition logic (PCL), a state-of-the-art approach for analyzing our composition. For this, we first articulate two properties that are of interest. Both properties are on the group key that is transported; we call them Group Key Confidentiality and Acquisition. Group Key Confidentiality is the property that if a principal possesses the key, then it is an authorized member of the group. Group Key Acquisition is the property that if a principal is a member of the group, then it is able to acquire the group key. In the course of our rigorous analysis, we discovered a flaw in our first design, which we point out, and which lead us to our second design. We have implemented both designs starting with the publicly available reference implementation of Kerberos, and an open-source implementation of MIKEY. Our implementations are available as open-source. We discuss our experience from the implementation, and present empirical results.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".