Research On Security Ranking Mechanism Of Big Data Based On Trust Evaluation

Big data security and privacy issues have become a major bottleneck affecting the healthy and rapid development of big data industry.Due to the large data system and the traditional information systems are different in many ways,to effectively protect the security and privacy of big data,it is not feasible to directly copy the traditional information system level protection work content.The key to solve this problem is to put forward an objective and scientific evaluation method for data security level.In this paper,the main content of the research is the big data security rate mechanism based on the trust evaluation.Through the analysis of the new features and new challenges brought about by the big data security and privacy issues,objective scientific extraction of elements affecting the security level of big data,using the independent weight coefficient to solve the problem of rating elements weight,combining with the trust computing category,the security technology protection mechanism that is based on the idea of trust evaluation,formed a complete set of big data security rating mechanism.The main research contents and contributions of this paper are as follows:Firstly,this paper puts forward the key elements which influencing the big data security classification.It is difficult to measure and divide the degree of importance of data in a large data environment from a security point of view.Based on the analysis of the research summary of the big data security and privacy protection,the key problems of the big security classification and the classification principle,extracting the eight key elements affecting the big data security classification from big data own characteristics and security attributes,based on these elements,using the independence weight coefficient thought,putting forward the big data security classification three-dimensional thinking,standardize the examination grading three-dimensional and evaluation methods,to solve the problem of weight examination grading factors,standardizing the checking and deciding method of three-dimensional,solved the problem of rating elements weight.Secondly,based on trust evaluation,a quantitative model of big data reliability is established.According to the authenticity of the big data,the problem that is the data itself is difficult to distinguish the objectivity,by using the method of trusted computing based on trust evaluation,established data reliability quantitative model based on trust evaluation,translating the data authenticity abstraction problem into data reliability objective quantization method,the quantitative work involved in the quantitative model of big data reliability like trust model establishing,trust evaluation mathematical model and data reliability computing modeling are described in detail.In addition,by introducing the data reliability attenuation factor,the relationship between attenuation factor and propagation quantity of nodes is discussed,also,the validity of data reliability calculation method is effectively guided.Combined with the big data security classification three-dimensional and data reliability quantification model to form a complete big data security rating mechanism.Thirdly,the effectiveness of the proposed model is verified on the experimental simulation platform.Establish the simulation model of the data security classification on big data transaction platform using the simulation soft-ware,the comparison results between the rating methods with trust evaluation mechanism and without trust evaluation mechanism shows the necessity of data reliability quantifying in the rating mechanism,proving the scientific and effectiveness of the proposed security evaluation mechanism based on trust evaluation.Finally,on the basis of analyzing the function and operation process of big data transaction platform,applying the proposed mechanism to an actual big data scene as a case analysis.The analysis and implementation of data security rating in this case is given to verify the applicability and effectiveness of the proposed mechanism.