Conifer: Centrally-Managed PKI with Blockchain-Rooted Trust
Bibliographic record
Abstract
Secure naming systems, or more narrowly public key infrastructures (PKIs), form the basis of secure communications over insecure networks. All security guarantees against active attackers come from a trustworthy binding between user-facing names, such as domain names, to cryptographic identities, such as public keys. By offering a secure, distributed ledger with highly decentralized trust, blockchains such as Bitcoin show promise as the root of trust for naming systems with no central trusted parties. PKIs based upon blockchains, such as Namecoin and Blockstack, have greatly improved security and resilience compared to traditional centralized PKIs. Yet blockchain PKIs tend to significantly sacrifice scalability and flexibility in pursuit of decentralization, hindering large-scale deployability on the Internet. We propose Conifer, a novel PKI with an architecture based upon CONIKS, a centralized transparency-based PKI, and Catena, a blockchain-agnostic way of embedding a permissioned log, but with a different lookup strategy. In doing so, Conifer achieves decentralized trust with security at least as strong as existing blockchain-based naming systems, yet without sacrificing the flexibility and performance typically found in centralized PKIs. We also present our reference implementation of Conifer, demonstrating how it can easily be integrated into applications. Finally, we use experiments to evaluate the performance of Conifer compared with other naming systems, both centralized and blockchain-based, demonstrating that it incurs only a modest overhead compared to traditional centralized-trust systems while being far more scalable and performant than purely blockchain-based solutions.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".