Bibliographic record
Abstract
Digital signature and hash algorithms are essential components of the blockchain. Bitcoin and Ethereum use the same digital signature scheme Elliptic Curve Digital Signature Algorithm (ECDSA). However, they use the different hash algorithms. Bitcoin chooses to use Secure Hash Algorithm (SHA), and Ethereum uses Keccak-256. This paper studies the digital signature ECDSA by looking into its design, implementation and security. ECDSA is a variant of Digital Signature Algorithm (DSA). It requires a shorter key length than Rivest–Shamir–Adleman (RSA), so it was preferred to use in the blockchain. Furthermore, this paper will also explore the design and implementation of SHA-256 and Keccak- 256. Bitcoin chose to use SHA-256 since it came out earlier than Keccak-256 with adequate security. Conversely, Keccak-256 is preferred by Ethereum since it has better performance and security compared to SHA-256. The role of SHA-256 and Keccak-256 in Bitcoin and Ethereum are also explored. SHA-256 and Keccak-256 are used in the blockchains’ proof-of-work (or proof-of-stake) and merkle tree structure. The paper will also look into their security by analyzing the result of possible attacks against them. In addition, the paper will provide some thoughts on the security of ECDSA, SHA-256 and Keccak-256 by analyzing their designs and possible attacks.
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 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 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".