How to profit from payments channels

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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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.

Original languageEnglish
Title of host publicationProceedings of the 24th Financial Cryptography and Data Security, Kota Kinabalu, Sabah, Malaysia
EditorsJ. Bonneau, N. Heninger
Place of PublicationCham
PublisherSpringer
Pages284-303
Number of pages20
ISBN (Electronic)978-3-030-51280-4
ISBN (Print)978-3-030-51279-8
DOIs
Publication statusPublished - 2020
EventFinancial Cryptography and Data Security 2020 - Kota Kinabalu, Malaysia
Duration: 10 Feb 202014 Feb 2020
Conference number: 24
https://fc20.ifca.ai/

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12059 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceFinancial Cryptography and Data Security 2020
CountryMalaysia
CityKota Kinabalu
Period10/02/2014/02/20
Internet address

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