On "identities", "names", "NAMES", "ROLES" and Security: A Manifesto.
Bibliographic record
Abstract
There is a great deal of confusion in the cryptology literature relating to various identity related issues. By “names” (lower case), we are referring to informal, personal ways that we indicate others; by “NAMES” (upper case) we are referring to official ways that we use to indicate others. Both of these concepts are often confused with “identity”, which is something else altogether, and with “ROLES”. These confusions can lead to insecurities in key exchange as well as in other internet activities that relate to identity. We discuss why we should not use names in protocols and why we cannot use identities. We discuss why, in a public-key infrastructure, we need to use NAMES in key-exchange protocols, and how they should be chosen and why they have to be unique, and why we should not use NAMES in session protocols. We also argue for the importance of secure ROLEs in key-exchange protocols.
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.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.012 | 0.025 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.011 |
| 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".