On the Blockchain-Based General-Purpose Public Key Infrastructure
Bibliographic record
Abstract
The past few years have witnessed unprecedented advancements in the Distributed Ledger Technology (DLT) and blockchain - a form of DLT. DLT has clearly expanded the applications landscape in various sectors of our lives ranging from banking to business, finance, industry, education, and so on. On the other hand, security plays a crucial part in the successful realization of such applications and services. To this end, cryptography is the primary mean to protect the applications, networks, infrastructure, and services from cyber-threats. However, the existing Public Key Infrastructure (PKI) is based on central Certificate Authority (CA) that can become a bottleneck and may affect the efficiency of the cryptographic protocols because of the overhead incurred by the verification of cryptographic signatures and certificates. Recently, blockchain has also been leveraged to aid PKI without the need for a central authority. In this spirit, in this paper, we develop and implement a blockchain-based PKI using open-source Hyperledger Sawtooth. The proposed blockchain-based approach helps to address the problems of the existing PKI such as compromised and misbehaving CAs.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".