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Cross-chain exchange by transaction dependence with conditional transaction method

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Abstract

Cross-chain exchange is an important way to promote cooperation and exchange between different blockchains. However, current cross-chain approaches mainly rely on smart contracts with essential pre-deployment steps, several of which need to be synchronized, resulting in low performance. In this paper, we propose an asynchronous cross-chain exchange model based on a transaction that embeds a control condition to determine whether to transfer assets. The condition is used to specify the paired transaction. In order to find which cross-chain transaction meets the condition, the fields of cross-chain transactions are compared mutually, including sender, receiver, value, blockchain identifier, etc. Meanwhile, the balance process in the blockchain consensus needs to be changed: subtracting the balance of the sender first, increasing the balance of the receiver when the conditions match, or returning the assets to the sender if the time expires. By embedding conditions in transactions, the step of pre-deploying smart contracts is eliminated, and transactions can be sent in parallel. The simulation results show that compared with smart contract-based method, the exchange waiting time is reduced by up to 73.4%.

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Notes

  1. https://interledger.org/interledger.pdf.

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Funding

This work was supported in part by the National Natural Science Foundation of China under Grant No. 61772352; National Key Research and Development Project under Grant Nos. 2020YFB1711800 and 2020YFB1707900; the Science and Technology Project of Sichuan Province under Grant Nos. 2019YFG0400, 2020YFG0479, 2020YFG0322, and the R&D Project of Chengdu City under Grant No. 2019-YF05-01790-GX.

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  1. Sichuan University - Wangjiang Campus, Chengdu, China

    Hong Su, Bing Guo, Jun Yu Lu & Xinhua Suo

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  1. Hong Su
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  2. Bing Guo
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  3. Jun Yu Lu
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  4. Xinhua Suo
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Correspondence to Hong Su.

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Su, H., Guo, B., Lu, J.Y. et al. Cross-chain exchange by transaction dependence with conditional transaction method. Soft Comput 26, 961–976 (2022). https://doi.org/10.1007/s00500-021-06577-5

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