<div class="csl-bib-body">
<div class="csl-entry">Avarikioti, G., Bastankhah, M., Maddah-Ali, M. A., Pietrzak, K., Svoboda, J., & Yeo, M. (2024). Route Discovery in Private Payment Channel Networks. In <i>DPM & CBT 2024 Pre-proceedings</i> (pp. 195–211). http://hdl.handle.net/20.500.12708/210627</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/210627
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dc.description.abstract
In this work, we explore route discovery in private payment channel networks. We first determine what “ideal” privacy for a routing protocol means in this setting. We observe that protocols achieving this strong privacy definition exist by leveraging Multi-Party Computation but they are inherently inefficient as they must involve the entire network. We then present protocols with weaker privacy guarantees but much better efficiency (involving only a small fraction of the nodes). The core idea is that both sender and receiver gossip a message which propagates through the network, and the moment any node in the network receives both messages, a path is found. In our first protocol the message is always sent to all neighbouring nodes with a delay proportional to the fees of that edge. In our second protocol the message is only sent to one
neighbour chosen randomly with a probability proportional to its degree.We additionally propose a more realistic notion of privacy in order to measure the privacy leakage of our protocols in practice. Our realistic notion of privacy challenges an adversary that join the network with a
fixed budget to create channels to guess the sender and receiver of a transaction upon receiving messages from our protocols. Simulations of our protocols on the Lightning network topology (for random transactions and uniform fees) show that 1) forming edges with high degree nodes is a more effective attack strategy for the adversary, 2) there is a tradeoff between the number of nodes involved in our protocols (privacy) and the optimality of the discovered path, and 3) our protocols involve a very small fraction of the network on average.
en
dc.language.iso
en
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dc.subject
Payment Channel Networks
en
dc.subject
Privacy
en
dc.subject
Bitcoin
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dc.subject
Route Discovery
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dc.subject
Lightning Network
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dc.title
Route Discovery in Private Payment Channel Networks
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.contributor.affiliation
Princeton University, United States of America (the)
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dc.contributor.affiliation
University of Minnesota Twin Cities
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dc.contributor.affiliation
TU Wien, Austria
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dc.contributor.affiliation
National University of Singapore, Singapore
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dc.description.startpage
195
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dc.description.endpage
211
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dc.type.category
Full-Paper Contribution
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tuw.booktitle
DPM & CBT 2024 Pre-proceedings
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tuw.peerreviewed
true
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tuw.researchTopic.id
I1
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tuw.researchTopic.name
Logic and Computation
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tuw.researchTopic.value
100
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tuw.publication.orgunit
E192-06 - Forschungsbereich Security and Privacy
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dc.description.numberOfPages
17
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tuw.author.orcid
0009-0006-6342-9105
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tuw.event.name
8th International Workshop on Cryptocurrencies and Blockchain Technology (CBT 2024: )
en
dc.description.sponsorshipexternal
ERC CoG ForM-SMArt
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dc.description.sponsorshipexternal
Austrian Science Fund (FWF)
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dc.description.sponsorshipexternal
Data-Driven Distributed Algorithms
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dc.relation.grantnoexternal
863818
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dc.relation.grantnoexternal
10.55776/COE12
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dc.relation.grantnoexternal
MOE-T2EP20122-0014
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tuw.event.startdate
19-09-2024
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tuw.event.enddate
19-09-2024
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tuw.event.online
On Site
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tuw.event.type
Event for scientific audience
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tuw.event.place
Bydgoszcz
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tuw.event.country
PL
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tuw.event.presenter
Avarikioti, Georgia
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wb.sciencebranch
Informatik
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wb.sciencebranch
Mathematik
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wb.sciencebranch.oefos
1020
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wb.sciencebranch.oefos
1010
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wb.sciencebranch.value
80
-
wb.sciencebranch.value
20
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item.openairecristype
http://purl.org/coar/resource_type/c_5794
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item.openairetype
conference paper
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item.fulltext
no Fulltext
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item.languageiso639-1
en
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item.grantfulltext
restricted
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item.cerifentitytype
Publications
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crisitem.author.dept
E192-06 - Forschungsbereich Security and Privacy
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crisitem.author.dept
Princeton University, United States of America (the)