体现奖惩的云用户行为信任分级方法  

A rewarding good and penalizing bad sorting method for cloud service user behavioural trust

作　　者：彭定洪 宋博 PENG Ding-hong;SONG Bo(Faculty of Management and Economics,Kunming University of Science and Technology,Kunming 650504,China;Institute of Quality Development,Kunming University of Science and Technology,Kunming 650504,China)

机构地区：[1]昆明理工大学管理与经济学院,昆明650504 [2]昆明理工大学质量发展研究院,昆明650504

出　　处：《控制与决策》2023年第12期3553-3561,共9页Control and Decision

基　　金：国家自然科学基金项目(71861018,61364016);云南省基础研究计划项目(202201AT070190);云南省哲学社会科学规划项目(YB2019067);中国博士后科学基金项目(2015T80990,2014M550473)。

摘　　要：云计算开放式的资源访问接口、海量资源集中化管理等优势无形中加大了云资源和其他云服务用户(CSU)受不可信CSU威胁的可能.为有效减少此类威胁,需对CSU实施不同信任等级的分别管控,其中对CSU行为信任分级至关重要.鉴于此,提出一种体现奖惩的犹豫模糊CSU行为信任分级求解途径.首先,基于TOPSISSort-C框架,以犹豫模糊集(HFS)刻画多来源差异化CSU行为信任数据,选取每一指标下各CSU行为信任水平分位数作为该指标分级阈值;然后,通过对不同信任水平的CSU行为数据加以非线性放缩,获取体现奖惩的CSU行为信任的强可信、不可信测度(MDT、MDD),使得CSU行为信任水平优劣更加直观且扩大分级区分度;最后,通过某互联网公司对CSU行为信任分级的实例辅之对比分析,验证所提出方法的有效性和增强奖优罚劣优势.The advantages of cloud computing such as open resource access interface and centralized management of massive resources invariably increase the possibility of cloud resources and other cloud service users(CSU) being threatened by untrustworthy CSUs.To effectively reduce the occurrence of such situations that threats to cloud security,it is necessary to implement separate controls for CSUs with different trust levels,and the discussion of the CSU behavioural trust sorting method is crucial.Therefore,a hesitant fuzzy CSU behaviour trust sorting approach with the function of rewarding good and penalizing bad is proposed.Based on the TOPSIS-Sort-C framework,the method uses a hesitant fuzzy set(HFS) to characterize the different CSU behavioural trust data from different sources,takes quantiles of each CSU behavioural trust indicator as the sorting thresholds.By non-linearly deflating the CSU behavioural data at different trust levels,the majorant degree of trust or distrust(MDT,MDD) of CSU behavioural reflecting reward or penalty are obtained to make the CSU behavioural trust level more intuitive while further expanding the graded differences.Finally,we confirm the effectiveness of the proposed method and the advantages of majorant reward or penalty by a comparison analysis with an example of CSU behaviour trust sorting in an internet company.

关 键 词：云计算 行为信任分级 犹豫模糊集(HFS) TOPSIS-Sort-C 强可信、不可信测度 

分 类 号：C934[经济管理—管理学]