A Context Aware Reputation Mechanism for Enhancing Big Data Veracity in Mobile Cloud Computing-猫眼课题宝

DOI：

10.1109/cit/iucc/dasc/picom.2015.304

发表时间：

2015-10

期刊：

2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Hui Lin;Jia Hu;Jiajia Liu;Li Xu;Yulei Wu

数据真实性确保所使用的数据是可信的、真实的，并受到保护，免受未经授权的访问和修改。为了实现大数据的准确性，必须设计和开发特定的信任模型和方法。为了提高移动的云计算（MCC）中大数据的准确性，提出了一种基于类别上下文感知和推荐激励的信誉机制（CCRM）来抵御内部攻击。仿真结果和性能分析表明，与MCC中已有的信誉机制相比，在内部合谋攻击和恶意中伤攻击下，CCRM在推荐者效用、信誉下降速度和更新准确性等方面具有上级性能.

Data veracity ensures that the data used are trusted, authentic and protected from unauthorized access and modification. In order to implement the veracity of big data, specific trust models and approaches must be designed and developed. In this paper, a category based context aware and recommendation incentive based reputation mechanism (CCRM) is proposed to defend against the internal attacks to enhance the big data veracity in mobile cloud computing (MCC). Simulation results and performance analysis demonstrate the superior performance of the CCRM in terms of the utility of the recommender, the reputation decrease speed and update accuracy, compared to the existing reputation mechanisms under internal collusion attacks and bad mouthing attacks in MCC.

A Context Aware Reputation Mechanism for Enhancing Big Data Veracity in Mobile Cloud Computing

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