Associative Blockchain for Decentralized PKI Transparency
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The conventional public key infrastructure (PKI) model, which powers most of the Internet, suffers from an excess of trust into certificate authorities (CAs), compounded by a lack of transparency which makes it vulnerable to hard-to-detect targeted stealth impersonation attacks. Existing approaches to make certificate issuance more transparent, including ones based on blockchains, are still somewhat centralized. We present decentralized PKI transparency (DPKIT): a decentralized client-based approach to enforcing transparency in certificate issuance and revocation while eliminating single points of failure. DPKIT efficiently leverages an existing blockchain to realize an append-only, distributed associative array, which allows anyone (or their browser) to audit and update the history of all publicly issued certificates and revocations for any domain. Our technical contributions include definitions for append-only associative ledgers, a security model for certificate transparency, and a formal analysis of our DPKIT construction with respect to the same. Intended as a client-side browser extension, DPKIT will be effective at fraud detection and prosecution, even under fledgling user adoption, and with better coverage and privacy than federated observatories, such as Google’s or the Electronic Frontier Foundation’s.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it