Dynamic distributed key infrastructures (DDKI) and dynamic identity verification and authentication (DIVA)
Bibliographic record
Abstract
Crypto systems that trusted computing relies upon are comprised of three parts: key creation, key management and key distribution. Prior to the advent of communications important keys like your birth certificate were provided manually. PKI asymmetric systems became the prevalent architecture because a mechanism was provided that allowed the distribution of keys in large communication (encryption or authentication) platforms. The flaws of that architecture were more of a nuisance at that point when compared to the benefit and lack of alternatives. Now, the ability to break and steal keys is an existential threat to that framework. Distributed key systems (like one-time-pad enigma systems) languished because of the requirement for manual distribution of keys. That encumbrance for distributed, trusted cyber and trusted computing systems has been overcome. It is now simple, secure and online to create large, distributed authentication and encryption platforms that utilize one-time-pad distributed keys and where there is only partial disclosure of credentials. This paper examines the comparison of asymmetric PKI and symmetric DDKI (Dynamic Distributed Key Infrastructure) handshakes. This paper also examines how to initiate secure communications with an endpoint/device/person that does not yet have a key without having to manually distribute the initial key.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".