《计算机应用》唯一官方网站 ›› 2024, Vol. 44 ›› Issue (7): 2087-2092.DOI: 10.11772/j.issn.1001-9081.2023081179

• 网络空间安全 • 上一篇    下一篇

基于变色龙哈希和可验证秘密共享的联盟链修改方法

宋宝燕, 丁俊翔, 王俊陆, 张浩林()   

  1. 辽宁大学 信息学部,沈阳 110036
  • 收稿日期:2023-09-01 修回日期:2023-10-08 接受日期:2023-10-17 发布日期:2024-07-18 出版日期:2024-07-10
  • 通讯作者: 张浩林
  • 作者简介:宋宝燕(1965—),女,辽宁铁岭人,教授,博士,CCF高级会员,主要研究方向:大规模图处理、大数据、流数据;
    丁俊翔(1998—),男,吉林辽源人,硕士研究生,主要研究方向:大规模图处理、大数据、流数据;
    王俊陆(1988—),男,辽宁丹东人,博士研究生,CCF会员,主要研究方向:大规模图处理、大数据、流数据;
    第一联系人:张浩林(1979—),男,山东蓬莱人,博士研究生,主要研究方向:大规模图处理、大数据、流数据。
  • 基金资助:
    国家重点研发计划项目(2021YFF0901004);辽宁省应用基础研究计划项目(2022JH2/101300250);辽宁大学青年科研基金项目(LDYBJB2301);辽宁省中央引导地方科技发展资金资助计划项目(2022JH6/100100032);辽宁省自然基金资助项目(2022-KF-13-06)

Consortium blockchain modification method based on chameleon hash and verifiable secret sharing

Baoyan SONG, Junxiang DING, Junlu WANG, Haolin ZHANG()   

  1. College of Computer Science,Liaoning University,Shenyang Liaoning 110036,China
  • Received:2023-09-01 Revised:2023-10-08 Accepted:2023-10-17 Online:2024-07-18 Published:2024-07-10
  • Contact: Haolin ZHANG
  • About author:SONG Baoyan, born in 1965, Ph. D., professor. Her research interests include large-scale graph processing, big data, stream data.
    DING Junxiang, born in 1998, M. S. candidate. His research interests include large-scale graph processing, big data, stream data.
    WANG Junlu, born in 1988, Ph. D. candidate. His research interests include large-scale graph processing, big data, stream data.
    First author contact:ZHANG Haolin, born in 1979, Ph. D. candidate. His research interests include large-scale graph processing, big data, stream data.
  • Supported by:
    National Key Research and Development Program of China(2021YFF0901004);Liaoning Province Applied Basic Research Program(2022JH2/101300250);Youth Scientific Research Fund Program of Liaoning University(LDYBJB2301);Liaoning Province Program of Central Leading Local Science and Technology Development Fund(2022JH6/100100032);Liaoning Provincial Natural Science Foundation(2022-KF-13-06)

摘要:

区块链具有去中心化、不可篡改、可追溯等特征。现有的联盟链系统在数据上链后会全程留痕,当出现敏感信息或恶意数据时无法处理,或处理后区块链分叉、中断。针对这些问题,提出一种基于变色龙哈希和可验证秘密共享的联盟链数据修改方法。首先,把变色龙哈希的陷门再分配给身份节点,从而将发起修改者与实际修改者进行隔离;其次,为保证再分配值的正确性,将不同时间周期变色龙哈希所对应的数据设为可验证数据,用验证节点上传承诺到可验证数据,并用提案节点通过承诺验证秘密共享值;最后,为防止节点作恶,提出基于奖励金机制的数据纠正方法提高节点纠正作恶的积极性,降低作恶的可能。在中山大学区块链与智能金融研究中心InPlusLab开发的DApps数据集上进行实验的结果表明:当恶意节点数30个时,所提方法相较于用传统变色龙哈希修改联盟链数据的方法在处理恶意节点的效率方面提高了44.1%;当恶意数据量达到30条时,在处理恶意数据的时间上缩短了53.7%。

关键词: 可修改联盟链, 变色龙哈希, 秘密共享, 可验证数据, 奖励金机制

Abstract:

Blockchain has the characteristics of decentralization, tamper resistance, and traceability. The existing consortium blockchain systems would leave traces throughout the entire process after uploading data, which can not work when sensitive information or malicious data appears, or the blockchains tend to fork or interrupt after processing. Therefore, a consortium blockchain data modification method based on chameleon hash and verifiable secret sharing was proposed to address these issues. Firstly, the trap doors of the chameleon hash were redistributed to the identity nodes, thereby isolating the initiator of modification and actual modifier. Secondly, in order to ensure the correctness of redistribution values, the data corresponding to chameleon hash in different time periods were set as verifiable data, the commitment was uploaded by the verification node to verifiable data, and the secret shared value was verified by the proposal node through the commitment. Finally, to prevent nodes from committing wrongdoing, a data correction method based on a reward mechanism was proposed, which increased the enthusiasm of nodes to correct wrongdoing and reduced the possibility of wrongdoing. The experiments were carried out on the DApps dataset developed by InPlusLab, the research center for blockchain and intelligent finance at Sun Yat-sen University. Experimental results show that compared to the traditional chameleon hash method for modifying consortium blockchain data,when the number of malicious nodes reaches 30, the consortium blockchain modification method based on chameleon hash and verifiable secret sharing improves the efficiency of processing malicious nodes by 44.1%; and when the amount of malicious data reached 30, the processing time for malicious data was shortened by 53.7%.

Key words: modifiable consortium blockchain, chameleon hash, secret sharing, verifiable data, reward mechanism

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