A Survey on Zero-Knowledge Proof in Blockchain
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
Blockchain, which is usually regarded as a public, decentralized and distributed ledger, has attracted significant attention recently. In the environment of blockchain, all historical transaction data are recorded and stored. However, because blockchain is open and transparent, a malicious user may illegally access private transaction data, including transaction amount, account address, and account balance. As a cryptographic technique, zero-knowledge proof (ZKP) can be used to verify whether the prover has enough transaction amount in the environment of blockchain without leaking any private transaction data. This article provides a comprehensive survey on ZKP in the environment of blockchain with the aim of highlighting security problems and challenges. It first discusses the framework, models and applications of ZKP. Next, it provides an introduction of blockchain, and proposes a framework of ZKP in the environment of blockchain. Then, it highlights the current state of ZKP in the environment of blockchain. Finally, it identifies some potential problems and future research directions.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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