基于数据消冗技术的隐私大数据属性加密仿真  

Simulation of Privacy Big Data Attribute Encryption Based on Data De Redundancy Technology

作　　者：陈小娟[1] 贺红艳[1] 张慧萍[1] CHEN Xiao-juan;HE Hong-yan;ZHANG Hui-ping(Engineering and Technology College,Hubei University of Technology,Wuhan Hubei 430068,China)

机构地区：[1]湖北工业大学工程技术学院,湖北武汉430068

出　　处：《计算机仿真》2022年第11期422-426,共5页Computer Simulation

摘　　要：针对海量大数据信息量大、增长速度快、数据多样化冗余度高的特征,导致加密效率低、传输实时性差问题,提出基于数据消冗技术的隐私大数据属性加密方法。使用Bloom过滤技术降低大数据的维数,利用hash函数计算出消冗过程中的误判率,并根据映射位数组确定最优扩列函数数量。同时在现有ABE研究基础上,使用优化的密文策略加密方案更有效完成云数据的安全共享访问,减少属性空间的数据元素存储尺寸,实现降低共享参数数量,保证大数据安全加密的有效性。仿真结果表明,所提算法可以有效地拦截攻击数据,提高大数据属性的安全性能,保证加密效率,具有较高的实用性。Generally,massive big data has large amount of information,fast growth rate,high data diversity redundancy,low encryption efficiency and poor real-time transmission.In this regard,we reported an attribute encryption method for privacy big data based on data redundancy elimination technology.Bloom filtering technology was introduced to reduce dimension of big data.Hash function was applied to calculate the error rate during the period of eliminating redundancy.Meanwhile,the number of optimal extension functions was determined via mapping the number of bits.Based on ABE,the optimized ciphertext policy encryption scheme was used to achieve the secure shared access of cloud data,reduce the storage size of data elements in attribute space and the number of shared parameters,and ensure the effectiveness of big data security encryption.The simulation results show that the algorithm has excellent big data attribute security performance,high encryption efficiency and practicability.

关 键 词：隐私大数据 冗余数据 安全共享访问 属性加密仿真 

分 类 号：TM214[一般工业技术—材料科学与工程]