Scalable transactional relationship protection based on minimized replications for permissioned blockchain

作     者：Li, Wenquan Min, Xinping Kong, Lanju Li, Qingzhong 

作者机构：Shandong Univ Sch Software Jinan 250010 Peoples R China Dareway Software Co Ltd Jinan 250000 Peoples R China 

出 版 物：《WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS》 (World Wide Web)

年 卷 期：2025年第28卷第1期

页      面：1-24页

核心收录：

学科分类：08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Research and Development Plan [2021YFF0704102] National Social Science Fund [20BJY131] Major Science and Technology Innovation of Shandong Province [2020CXGC010106, 2021CXGC010108] Natural Science Foundation of Shandong Province [ZR201911130645] Industrial Experts Program of Spring City 

主　　题：Blockchain Partial replication Transactional relationship Privacy Scalability 

摘      要：The permissioned blockchain is one of the core technologies for Web3.0. However, the transactional relationship leakage on blockchain has become a critical threat to the benefits of users. To prevent the malicious analysis of the sending and receiving addresses of series of transactions, much effort has recently been put into transactional relationship protection (TRP) in blockchain by academia and industry. However, most of the current TRP methods are designed for the particular fungible cryptocurrencies, which have limitations in terms of asset types and scenarios. This paper proposes a TRP-enabled permissioned blockchain framework. First, the framework introduces a ledger structure comprising two distinct types of blocks. The basic block publicly contains the verifiable structure of the transactions, while the transaction block privately contains their content in selected committee. Second, to prevent the committee from analysing the relationships in transaction blocks, the framework includes a confidential transaction replication mechanism that splits the related transactions and replicates them to different committees. Furthermore, we optimize the framework via quantitative analysis to minimize the required replicating size of per transaction, thus enabling the framework to achieve enhanced privacy and scalability. Theoretical analysis and experimental results on datasets demonstrate that the framework achieves more than 95% probabilities of hiding the relationships, and maintains 10 times the throughput compared to the blockchain without our method.