Bitcoin-Compatible Privacy-Preserving Multi-Party Payment Channels Supporting Variable Amounts

Blockchain and cryptocurrencies are developing rapidly, and the scalability issue has become a constraint on their practical application and development. Off-chain payment channel is an effective solution to the scalability problem of blockchain. Currently, various payment channel protocols have been proposed. However, privacy issues are vital in payment channels. Existing works that consider privacy issues mainly focus on payment channel networks and payment channel hubs, while there is little work on two-party and multi-party channels. In addition, many existing payment channel works that consider privacy protection fix the transaction amounts to ensure the hiding of payment relationships or rely on smart contracts, which will hinder the practical application of payment channels. In this work, we propose a two-party privacy-preserving payment channel protocol that is compatible with Bitcoin (TBPChannel), achieving value privacy and unlinkability, while supporting variable transaction amounts. On this basis, we propose a privacy-preserving multi-party payment channel protocol (MBPChannel), which removes the role of untrusted operators in previous multi-party settings and further achieves robustness. We formally model the protocols in the universal composability framework and prove the security. Finally, we implement the protocols and provide a performance evaluation. The results demonstrate the scalability and practicality of our protocols. Compared to current protocols, even though we use privacy-preserving methods, our protocols are still efficient and applicable in practice.

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Publication Details

Journal
IEEE Transactions on Information Forensics and Security
Published
2025-01-01
DOI
https://doi.org/10.1109/tifs.2025.3528827
Citations
1
Primary Topic
Advanced Steganography and Watermarking Techniques
Type
article
Field-Weighted Citation Impact
1.28

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article

Bitcoin-Compatible Privacy-Preserving Multi-Party Payment Channels Supporting Variable Amounts

Ruonan Chen, Jianying Zhou, Yizhong Liu, Qianhong Wu et al.
1 citations
IEEE Transactions on Information Forensics and Security
Advanced Steganography and Watermarking Techniques
1.28
article

Bitcoin-Compatible Privacy-Preserving Multi-Party Payment Channels Supporting Variable Amounts

Ruonan Chen, Jianying Zhou, Yizhong Liu, Qianhong Wu, Willy Susilo, Dawei Li, Yang Zhang, Jianwei Liu
article en
1 citations

Abstract

Blockchain and cryptocurrencies are developing rapidly, and the scalability issue has become a constraint on their practical application and development. Off-chain payment channel is an effective solution to the scalability problem of blockchain. Currently, various payment channel protocols have been proposed. However, privacy issues are vital in payment channels. Existing works that consider privacy issues mainly focus on payment channel networks and payment channel hubs, while there is little work on two-party and multi-party channels. In addition, many existing payment channel works that consider privacy protection fix the transaction amounts to ensure the hiding of payment relationships or rely on smart contracts, which will hinder the practical application of payment channels. In this work, we propose a two-party privacy-preserving payment channel protocol that is compatible with Bitcoin (TBPChannel), achieving value privacy and unlinkability, while supporting variable transaction amounts. On this basis, we propose a privacy-preserving multi-party payment channel protocol (MBPChannel), which removes the role of untrusted operators in previous multi-party settings and further achieves robustness. We formally model the protocols in the universal composability framework and prove the security. Finally, we implement the protocols and provide a performance evaluation. The results demonstrate the scalability and practicality of our protocols. Compared to current protocols, even though we use privacy-preserving methods, our protocols are still efficient and applicable in practice.

IEEE Transactions on Information Forensics and SecurityVol. 20
Singapore University of Technology and Design (SG), University of Wollongong (AU), Beihang University (CN)
National Natural Science Foundation of China, Beijing Municipal Natural Science Foundation, Key Technologies Research and Development Program
Openalex Percentile: Top 24%
Advanced Steganography and Watermarking Techniques
1.28
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