1.齐鲁工业大学(山东省科学院),山东省计算中心(国家超级计算济南中心), 算力互联网与信息安全教育部重点实验室,山东 济南 250014
2.山东省工业网络和信息系统安全重点实验室,山东省基础科学研究中心(计算机科学),山东 济南 250014
[ "田汉文(1998- ),女,山东临沂人,齐鲁工业大学(山东省科学院)硕士生,主要研究方向为数据安全、区块链。" ]
[ "张淑慧(1981- ),女,山东济宁人,齐鲁工业大学(山东省科学院)/山东省计算中心(国家超级计算济南中心)研究员,主要研究方向为数据安全、区块链、计算机取证。" ]
[ "王连海(1969- ),男,山东潍坊人,齐鲁工业大学(山东省科学院)/山东省计算中心(国家超级计算济南中心)研究员,主要研究方向为数据安全、区块链、计算机取证。" ]
[ "徐淑奖(1979- ),男,山东临沂人,齐鲁工业大学(山东省科学院)/山东省计算中心(国家超级计算济南中心)研究员,主要研究方向为数据安全、区块链。" ]
[ "邵蔚(1994- ),女,山东济南人,齐鲁工业大学(山东省科学院)/山东省计算中心(国家超级计算济南中心)研究员,主要研究方向为区块链、隐私保护。" ]
收稿:2024-03-28,
修回:2024-08-09,
纸质出版:2025-02-25
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田汉文, 张淑慧, 王连海, 等. 信誉驱动的区块链分片共识及激励机制设计[J]. 网络与信息安全学报, 2025, 11(1): 141-150.
TIAN H W, ZHANG S H, WANG L H, et al. Design of reputation-driven blockchain sharding consensus and incentive mechanism[J]. Chinese Journal of Network and Information Security, 2025, 11(1): 141-150.
田汉文, 张淑慧, 王连海, 等. 信誉驱动的区块链分片共识及激励机制设计[J]. 网络与信息安全学报, 2025, 11(1): 141-150. DOI: 10.11959/j.issn.2096-109x.2025002.
TIAN H W, ZHANG S H, WANG L H, et al. Design of reputation-driven blockchain sharding consensus and incentive mechanism[J]. Chinese Journal of Network and Information Security, 2025, 11(1): 141-150. DOI: 10.11959/j.issn.2096-109x.2025002.
当前,实用性拜占庭容错(PBFT,practical Byzantine fault tolerance)共识算法在区块链分片环境下面临通信复杂度高、领导节点选举随机、共识节点不安全以及共识系统缺乏激励等问题。而信誉机制可以根据节点的行为和表现进行评估和奖惩,提高系统对节点的信任度,并促进节点的积极参与和诚实行为。基于此,提出了信誉驱动的区块链分片共识及激励机制。在基于信誉管理的分片系统中,采用聚合签名技术将多个共识节点的签名合并为一个,通过信誉评估确定节点的投票权重,大大减少了通信开销并提高了共识的平等性和安全性;同时,在信誉管理下提出一种押金与激励机制,当节点初入网络时设置最低门票标准,并根据信誉值随时间逐步增加,根据节点的角色和分工的不同,采用基础奖励与有效奖励结合的激励模式,从而有效平衡节点的稳定收益和动态奖励,不断提升网络的稳定性和安全性。实验结果显示,所提共识算法R-PBFT相比PBFT,具有较小的时延和较高的吞吐量,有效地提高了系统性能;与单纯按照信誉等比分配的激励机制相比,所提激励机制具有更高的共识参与率。
The PBFT (Practical Byzantine Fault Tolerance) consensus algorithm was confronted with several challenges in the blockchain sharding environment
including high communication complexity
random leader election
node insecurity
and insufficient participation incentives. To address these issues
a reputation-driven blockchain sharding consensus and incentive mechanism was proposed. Reputation mechanisms were utilized to evaluate and incentivize node behavior
thereby enhancing system trust and promoting active participation. Aggregated signature technology was employed to merge consensus node signatures
and node voting weights were determined based on reputation assessments
which reduced communication overhead and improved consensus equality and security. Additionally
a deposit and incentive mechanism was introduced under reputation management
where initial entry standards were set and gradually increased over time according to reputation values. Differentiated incentive models
based on node roles
were designed to balance stable income and dynamic rewards
thereby continuously improving network stability and security. Simulation results demonstrated that the proposed R-PBFT consensus algorithm outperformed the traditional PBFT
maintaining lower latency and higher throughput
which effectively enhanced system performance. Furthermore
compared to proportional incentive allocation based solely on reputation
the proposed incentive mechanism achieves higher consensus participation rates.
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