Starfish: Rebalancing Multi-Party Off-Chain Payment Channels
Minghui Xu, Wenxuan Yu, Guangyong Shang, Guangpeng Qi, Dongliang Duan, Shan Wang, Kun Li, Yue Zhang, Xiuzhen Cheng
TL;DR
Starfish tackles PCN scalability by centralizing rebalancing around hub nodes in a star-shaped topology and merging multiple independent channels with a single MERGE contract. The design partitions the hub’s funding into edge allocations, uses per-edge versioning to prevent double spending, and supports capacity transfers via Update Merge with atomic broadcasting, while preserving safety through formal UC security analysis. An Ethereum-based prototype and Lightning Network–driven simulations show Starfish achieves higher off-chain payment success and lower gas costs than prior approaches like Revive and Shaduf, particularly in dense topologies and skewed payment patterns. This approach offers a practical, provably secure path to large-scale, low-latency off-chain transactions in real-world PCNs with minimal on-chain overhead.
Abstract
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.