SAMCU: Secure and Anonymous Multi-Channel Updates in Payment Channel Networks

Jianhuan Wang, Shang Gao, Guyue Li, Keke Gai, Bin Xiao

Abstract: The Payment Channel Network (PCN) has emerged as an extensively adopted solution to address the scalability issues of Bitcoin by efficient off-chain updates. However, conflicts arise while existing update protocols are pursuing multiple goals of security, privacy, and expressiveness. In this work, we propose a new off-chain update protocol, Secure and Anonymous Multi-Channel Updates (SAMCU), which is developed on the basis of Unspent Transaction Output (UTXO). SAMCU aims at achieving goals of internal anonymity, balance security, and multi-channel updates simultaneously, which has not been done before. To achieve these goals, we exploit the technique of updating graph splitting (UGS) to make participants aware of only the identities of their neighboring sub-graphs, thereby ensuring internal anonymity in multi-channel updates. Then, to avoid security issues arising from equal sub-graphs, we further propose an Enable Payment Transaction Tree (EPTT) to guarantee balance security for each honest protocol participant. Moreover, we optimize the performance of our solution, reducing transaction fees by splitting transactions and the number of communication connections by hierarchical communication. To evaluate the performance of the SAMCU, we implement a prototype involving up to 100 updating payment channels. Experimental results demonstrate that SAMCU outperforms the state-of-the-art, resulting in approximately 70% savings in communication connections and a 66% reduction in on-chain transaction fees when the number of updating payment channels is 100.