On Selfholding Attack Impact on Imperfect PoW Blockchain Networks
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
Proof-of-Work (PoW) blockchain systems like Bitcoin and Ethereum are vulnerable to selfholding attack. The prior modeling-based works about this attack only considered Bitcoin and assumed that there were at most two honest pools in a perfect network (no natural fork in such networks). However, a blockchain network is imperfect due to block propagation delay, which can lead to forking. Moreover, there may be more than two pools under attack. This paper aims for a quantitative analysis of an imperfect PoW blockchain network system under selfholding attack. We develop a novel stochastic model and derive formulas to evaluate the effect of selfholding attack on miner revenue, system security and system performance. Our work can be used to analyze the scenario where there are any number of pools suffering selfholding attack in both Ethereum and Bitcoin. The model in this paper can capture the behaviors of a more realistic and more general scenario, compared with the existing models. Moreover, our model and formulas can also be applied to evaluate a blockchain system, which uses a similar reward mechanism and is vulnerable to selfholding attack. Our work can help design a more secure blockchain incentive mechanism and an in-pool reward mechanism.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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