How to profit from payments channels

Conference Paper (2020)
Author(s)

Oguzhan Ersoy (TU Delft - Cyber Security)

S. Roos (TU Delft - Data-Intensive Systems)

Zekeriya Erkin (TU Delft - Cyber Security)

Research Group
Cyber Security
Copyright
© 2020 O. Ersoy, S. Roos, Z. Erkin
DOI related publication
https://doi.org/10.1007/978-3-030-51280-4_16
More Info
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Publication Year
2020
Language
English
Copyright
© 2020 O. Ersoy, S. Roos, Z. Erkin
Research Group
Cyber Security
Bibliographical Note
Coronavirus update: Authors whose travel is disrupted can arrange to give video presentations@en
Volume number
12059
Pages (from-to)
284-303
ISBN (print)
978-3-030-51279-8
ISBN (electronic)
978-3-030-51280-4
Reuse Rights

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Abstract

Payment channel networks like Bitcoin’s Lightning network are an auspicious approach for realizing high transaction throughput and almost-instant confirmations in blockchain networks. However, the ability to successfully conduct payments in such networks relies on the willingness of participants to lock collateral in the network. In Lightning, the key financial incentive to lock collateral are low fees for routing payments of other participants. While users can choose these fees, real-world data indicates that they mainly stick to default fees. By providing insights on beneficial choices for fees, we aim to incentivize users to lock more collateral and improve the effectiveness of the network. In this paper, we consider a node that given the network topology and the channel details establishes channels and chooses fees to maximize its financial gain. Our contributions are i) formalization of the optimization problem, ii) proving that the problem is NP-hard, and iii) designing and evaluating a greedy algorithm to approximate the optimal solution. In each step, our greedy algorithm establishes a channel that maximizes the increase to ’s total reward, which corresponds to maximizing the number of shortest paths passing through. Our simulation study leveraged real-world data sets to quantify the impact of our gain optimization and indicates that our strategy is at least a factor two better than other strategies.

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