基于证据理论信任模型的众包质量监控  

Crowdsourcing quality control based on reputation model of Dempster-Shafer theory

作　　者：阮闪闪 王小平[1] 薛小平[1] 

机构地区：[1]同济大学电子与信息工程学院,上海201804

出　　处：《计算机应用》2015年第8期2380-2385,共6页journal of Computer Applications

基　　金：国家自然科学基金资助项目(60972036)

摘　　要：针对现有众包系统不能快速有效地检测众包交互过程中的恶意行为现状,从信誉角度提出基于证据理论的信任评估模型(DS_CQC)来实现众包平台的质量监控。首先,基于时间窗口获得持续可信证据序列和持续不可信证据序列;其次,从证据重要性、证据间关联和证人可信度三方面对原始D-S证据理论进行改进,获得改进的基本概率信度函数;最后,利用改进的D-S证据理论对证据序列进行融合,计算其直接信誉和间接信誉,最终获得接包方的综合信誉。模型中引入奖惩机制,用以激励接包方参与众包并提供高质量众包,同时遏制恶意的接包方。通过仿真实验和对真实众包数据的实验表明,与基于概率的信任模型相比,DS_CQC检测出持续恶意工作者、策略性恶意工作者的速度和效率至少分别提高了50%和3.1%,具有较强的抗攻击能力。Since the existing crowdsourcing model could not detect the malicious behavior in the crowdsourcing system quickly and efficiently, a reputation model based on Dempster-Shafer theory, called DS_CQC （ Dempster/Shafer Crowdsoueing Quality Control）, was proposed to apply to the crowdsourcing quality control. Firstly, the sustainable credible evidence sequence and sustained incredible evidence sequence based on time-window were obtained. Secondly, the original D-S evidence theory was improved through three aspects including importance of evidence, relationship of evidence and reliability of witness, and the new basic probability function was acquired. Finally, evidence sequence was fused by using the improved D-S evidence theory and then the direct reputation, indirect reputation and comprehensive reputation were computed. The incentive mechanism based on reputation was used to encourage people to participate in crowdsourcing actively and submit a higher quality crowd, while the malicious workers were suppressed. Experiments on simulation and real crowd data were conducted, and compared to the trust model of probability, the detection of malicious behavior in the erowdsourcing system of DS_CQC increased by 50% in speed and 3.1% in efficiency at least. The result proves that the DS_CQC has the high anti- attacking capability.

关 键 词：众包 质量监控 证据理论 信任模型 恶意检测 抗攻击 

分 类 号：TP311.5[自动化与计算机技术—计算机软件与理论]