Bandwidth-Efficient Transaction Relay for Bitcoin
Bibliographic record
Abstract
Bitcoin is a top-ranked cryptocurrency that has experienced huge growth and survived numerous attacks. The protocols making up Bitcoin must therefore accommodate the growth of the network and ensure security. Security of the Bitcoin network depends on connectivity between the nodes. Higher connectivity yields better security. In this paper we make two observations: (1) current connectivity in the Bitcoin network is too low for optimal security; (2) at the same time, increasing connectivity will substantially increase the bandwidth used by the transaction dissemination protocol, making it prohibitively expensive to operate a Bitcoin node. Half of the total bandwidth needed to operate a Bitcoin node is currently used to just announce transactions. Unlike block relay, transaction dissemination has received little attention in prior work. We propose a new transaction dissemination protocol, Erlay, that not only reduces the bandwidth consumption by 40% assuming current connectivity, but also keeps the bandwidth use almost constant as the connectivity increases. In contrast, the existing protocol increases the bandwidth consumption linearly with the number of connections. By allowing more connections at a small cost, Erlay improves the security of the Bitcoin network. And, as we demonstrate, Erlay also hardens the network against attacks that attempt to learn the origin node of a transaction. Erlay is currently being investigated by the Bitcoin community for future use with the Bitcoin protocol.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".