Avarikioti, Z., Pietrzak, K., Salem, I., Schmid, S., Tiwari, S., & Yeo, M. 2022, May 2–6 Hide & seek: privacy-preserving rebalancing on payment channel networks. Unpublished paper presented at Financial Cryptography and Data Security 2022.
Added by: Rucknium (2022-05-05 20:31)
|Resource type: Conference Paper
BibTeX citation key: Avarikioti2022
View all bibliographic details
|Categories: Not Monero-focused
Keywords: Payment Channels
Creators: Avarikioti, Pietrzak, Salem, Schmid, Tiwari, Yeo
Collection: Financial Cryptography and Data Security 2022
|Attachments 152.pdf [51/278]||URLs https://fc22.ifca. ... roceedings/152.pdf|
Payment channels effectively move the transaction load off-chain thereby successfully addressing the
inherent scalability problem most cryptocurrencies face. A major drawback of payment channels is the need to “top
up” funds on-chain when a channel is depleted. Rebalancing was proposed to alleviate this issue, where parties with
depleting channels move their funds along a cycle to replenish their channels off-chain. Protocols for rebalancing so
far either introduce local solutions or compromise privacy.
In this work, we present an opt-in rebalancing protocol that is both private and globally optimal, meaning our protocol
maximizes the total amount of rebalanced funds. We study rebalancing from the framework of linear programming.
To obtain full privacy guarantees, we leverage multi-party computation in solving the linear program, which is
executed by selected participants to maintain efficiency. Finally, we efficiently decompose the rebalancing solution
into incentive-compatible cycles which conserve user balances when executed atomically.