Abstract
The payment channel networks (PCN) technique effectively improves the transaction efficiency of a blockchain system, further promotes its practical application. Atomic Multi-Path Payments (AMP) are usually used, in payment channel networks, to divide transactions to improve routing efficiency, thereby improving the transaction throughput. Improper transaction division, however, may increase the occurrence of routing failure. Therefore, how to perform efficient transaction partitioning is an urgent problem to be solved. In this work, we propose an improved transaction partition method, named Proportional Atomic Multi-Path Payments (PAMP), which can enhance the efficiency of transaction routing. The key insight of PAMP is that, when a transaction is executed, the trade share can be well divided by the remaining capacity in multiple channels, which can greatly improve the routing efficiency and maintain the balance of channel capacity in the network. Simulation results show that, in contrast to traditional routing algorithms, the transaction success rate is increased by 2.3%, and the average execution time is reduced by 75.09 ms. PAMP improves the transaction routing efficiency, and also promotes the balance of network channel capacity.
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Notes
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A credit card brand that is widely used around the world.
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Acknowledgements
This study is supported by the Foundation of National Natural Science Foundation of China (Grant Number: 62072273, 72111530206, 61962009, 61873117, 61832012, 61771231, 61771289); Natural Science Foundation of Shandong Province (ZR2019MF062); Shandong University Science and Technology Program Project (J18A326); Guangxi Key Laboratory of Cryptography and Information Security (No: GCIS202112); The Major Basic Research Project of Natural Science Foundation of Shandong Province of China (ZR2018ZC0438); Major Scientific and Technological Special Project of Guizhou Province (20183001), Foundation of Guizhou Provincial Key Laboratory of Public Big Data (No. 2019BD-KFJJ009), Talent project of Guizhou Big Data Academy. Guizhou Provincial Key Laboratory of Public Big Data ([2018]01).
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Guo, J., Shang, L., Wang, Y., Liang, T., Wang, Z., An, H. (2023). PAMP: A New Atomic Multi-Path Payments Method with Higher Routing Efficiency. In: Xu, Y., Yan, H., Teng, H., Cai, J., Li, J. (eds) Machine Learning for Cyber Security. ML4CS 2022. Lecture Notes in Computer Science, vol 13656. Springer, Cham. https://doi.org/10.1007/978-3-031-20099-1_48
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