云环境下高机密性数据分离销毁优化仿真研究

Optimized Simulation of High Secret Data Separation and Destruction in Cloud Environment

云环境下对高机密性数据的分离销毁过程进行优化,可有效提升云环境下用户存储数据信息的安全性。对数据分离销毁,需要对数据等级属性特征进行分类,利用决策函数完成对机密性数据的分离销毁。传统方法利用数据时间可预测性实现划其分离,但忽略了对数据进行合理分类,导致销毁效果不理想。提出基于支持向量机理论的云环境下高机密性数据分离销毁方法。获取高机密性数据属性向量集合,得到高机密性数据属性向量映射函数,组建高机密性数据判定模型,计算出高机密性数据在某时段内的最小判定误差能量,结合支持向量机理论对等级属性特征进行分类,得到支持向量机决策函数,依据上述函数完成对云环境下不同等级的高机密性数据分离销毁。实验结果表明,所提方法进行云环境下高机密性数据分离离销毁效果较高。

This article proposes a method for separation and destruction of high confidentiality data in cloud envi- ronment based on support vector machine （SVM）. Firstly, our research acquired attribute vector set of high confi- dentiality data and obtained mapping function of attribute vector, then built judging model to work out minimum jud- ging error energy of high confidentiality data in a certain period. We also classified level attribute character integrated with SVM and obtained decision function of SVM. Finally, we completed the separation and destruction with different levels. Experimental results show that the method has better effect of data separation and destruction and robustness is strong.