Settling Payments Fast and Private: Efficient Decentralized Routing for Path-Based Transactions
Bibliographic record
Abstract
Decentralized path-based transaction (PBT) networks maintain local payment channels between participants.Pairs of users leverage these channels to settle payments via a path of intermediaries without the need to record all transactions in a global blockchain.PBT networks such as Bitcoin's Lightning Network and Ethereum's Raiden Network are the most prominent examples of this emergent area of research.Both networks overcome scalability issues of widely used cryptocurrencies by replacing expensive and slow on-chain blockchain operations with inexpensive and fast off-chain transfers.At the core of a decentralized PBT network is a routing algorithm that discovers transaction paths between sender and receiver.In recent years, a number of routing algorithms have been proposed, including landmark routing, utilized in the decentralized IOU credit network SilentWhispers, and Flare, a link state algorithm for the Lightning Network.However, the existing efforts lack either efficiency or privacy, as well as the comprehensive analysis that is indispensable to ensure the success of PBT networks in practice.In this work, we first identify several efficiency concerns in existing routing algorithms for decentralized PBT networks.Armed with this knowledge, we design and evaluate SpeedyMurmurs, a novel routing algorithm for decentralized PBT networks using efficient and flexible embedding-based path discovery and on-demand efficient stabilization to handle the dynamics of a PBT network.Our simulation study, based on real-world data from the currently deployed Ripple credit network, indicates that SpeedyMurmurs reduces the overhead of stabilization by up to two orders of magnitude and the overhead of routing a transaction by more than a factor of two.Furthermore, using SpeedyMurmurs maintains at least the same success ratio as decentralized landmark routing, while providing lower delays.Finally, SpeedyMurmurs achieves key privacy goals for routing in decentralized PBT networks.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".