Bitforest: a Portable and Efficient Blockchain-Based Naming System
Bibliographic record
Abstract
Public key infrastructures (PKIs), or more generally secure naming systems, lie at the foundation of the security of any communication system. Without a trustworthy binding between user-facing names, such as domain names, and cryptographic identities, such as public keys, all security guarantees against active attackers come crashing down like a house of cards. Blockchains such as Bitcoin, by offering a decentralized yet secure public ledger, show promise as the root of trust for naming systems with no central trusted parties, greatly increasing their security compared to traditional centralized PKIs. Yet blockchain PKIs such as Namecoin and Blockstack tend to significantly sacrifice scalability and flexibility in pursuit of decentralization, hindering large-scale deployability on the Internet. We propose Bitforest, a secure naming system with an architecture combining a centralized yet only partially trusted name server with efficiently queryable verification data embedded in a novel data structure inside a cryptocurrency blockchain. Bitforest achieves decentralized trust and security as strong as existing blockchain-based naming systems while retaining most of the flexibility and performance of centralized PKIs, allowing fully validating thin clients to look up and verify name bindings with comparable efficiency to traditional systems. We use both numerical simulation and real-world experiments to evaluate the performance of Bitforest compared with other naming systems, both centralized and blockchain-based, showing that its performance goals are indeed achieved.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".