Starfish: Rebalancing Multi-Party Off-Chain Payment Channels
By: Minghui Xu , Wenxuan Yu , Guangyong Shang and more
Potential Business Impact:
Makes online payments faster and more reliable.
Blockchain technology has revolutionized the way transactions are executed, but scalability remains a major challenge. Payment Channel Network (PCN), as a Layer-2 scaling solution, has been proposed to address this issue. However, skewed payments can deplete the balance of one party within a channel, restricting the ability of PCNs to transact through a path and subsequently reducing the transaction success rate. To address this issue, the technology of rebalancing has been proposed. However, existing rebalancing strategies in PCNs are limited in their capacity and efficiency. Cycle-based approaches only address rebalancing within groups of nodes that form a cycle network, while non-cycle-based approaches face high complexity of on-chain operations and limitations on rebalancing capacity. In this study, we propose Starfish, a rebalancing approach that captures the star-shaped network structure to provide high rebalancing efficiency and large channel capacity. Starfish requires only $N$-time on-chain operations to connect independent channels and aggregate the total budget of all channels. To demonstrate the correctness and advantages of our method, we provide a formal security proof of the Starfish protocol and conduct comparative experiments with existing rebalancing techniques.
Similar Papers
Boosting Payment Channel Network Liquidity with Topology Optimization and Transaction Selection
Distributed, Parallel, and Cluster Computing
Helps online money transfers work faster and cheaper.
Hypergraph based Multi-Party Payment Channel
Distributed, Parallel, and Cluster Computing
Lets online money move faster and safer.
SCOOP: CoSt-effective COngestiOn Attacks in Payment Channel Networks
Cryptography and Security
Disrupts online money transfers by causing slowdowns.