Get Me Out of This Payment! Bailout: An HTLC Re-routing Protocol

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Abstract

The Lightning Network provides almost-instant payments to its parties. In addition to direct payments requiring a shared payment channel, parties can pay each other in the form of multi-hop payments via existing channels. Such multi-hop payments rely on a 2-phase commit protocol to achieve balance security; that is, no honest intermediary party loses her coins. Unfortunately, failures or attacks in this 2-phase commit protocol can lead to coins being committed (locked) in a payment for extended periods of time (in the order of days in the worst case). During these periods, parties cannot go offline without losing funds due to their existing commitments, even if they use watchtowers. Furthermore, they cannot use the locked funds for initiating or forwarding new payments, reducing their opportunities to use their coins and earn fees. We introduce Bailout, the first protocol that allows intermediary parties in a multi-hop payment to unlock their coins before the payment completes by re-routing the payment over an alternative path. We achieve this by creating a circular payment route starting from the intermediary party in the opposite direction of the original payment. Once the circular payment is locked, both payments are canceled for the intermediary party, which frees the coins of the corresponding channels. This way, we create an alternative route for the ongoing multi-hop payment without involving the sender or receiver. The parties on the alternative path are incentivized to participate through fees. We evaluate the utility of our protocol using a real-world Lightning Network snapshot. Bailouts may fail due to insufficient balance in alternative paths used for re-routing. We find that attempts of a node to bailout typically succeed with a probability of more than 94% if at least one alternative path exists.

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APA

Ersoy, O., Moreno-Sanchez, P., & Roos, S. (2024). Get Me Out of This Payment! Bailout: An HTLC Re-routing Protocol. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13951, pp. 92–109). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-47751-5_6

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