移动云计算环境下动态数据隐私密码机制仿真
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摘要
数据加密是当前保障数据隐私安全的主要途径,针对现有隐私保护机制的加密过程中,部分加密数据特征明显易被破解,以及加密过程耗时长等问题,提出一种基于随机树的动态数据隐私密码机制。以云计算动态数据的定义域、值域以及所设密钥为依据,构建数据随机树;通过随机树父节点随机分解完成动态数据的区域划分,根据得到的与数据点对应的子节点的值域计算父节点之间的条件熵。结合条件熵值进行数据重构,利用模糊函数对重构数据进行映射,得到数据伪随机序列,完成动态数据加密。仿真证明,所提数据隐私保护机制的加密与解密时间更少,隐私保护效果更好。
The data encryption is the main way to ensure data privacy and data security. The encryption process of existing privacy protection mechanisms need to take a long time. Therefore,we focus on a dynamic data privacy password mechanism based on random tree. Based on the definition domain,value domain and secret key of cloud computing dynamic data,we built a data random tree. According to the parent node of random tree,we carried out the random decomposition to complete regional division of dynamic data. According to the range of values of sub nodes corresponding to data points,we calculated the conditional entropy between parent nodes. Combined with the conditional entropy,we reconstructed the data. Finally,we used the fuzzy function to map the reconstructed data and obtained the data pseudo-random sequence. Thus,we could complete the dynamic data encryption. Simulation proves that the proposed data privacy protection mechanism needs less time for encryption and decryption. Meanwhile,it has better privacy protection.
The data encryption is the main way to ensure data privacy and data security. The encryption process of existing privacy protection mechanisms need to take a long time. Therefore,we focus on a dynamic data privacy password mechanism based on random tree. Based on the definition domain,value domain and secret key of cloud computing dynamic data,we built a data random tree. According to the parent node of random tree,we carried out the random decomposition to complete regional division of dynamic data. According to the range of values of sub nodes corresponding to data points,we calculated the conditional entropy between parent nodes. Combined with the conditional entropy,we reconstructed the data. Finally,we used the fuzzy function to map the reconstructed data and obtained the data pseudo-random sequence. Thus,we could complete the dynamic data encryption. Simulation proves that the proposed data privacy protection mechanism needs less time for encryption and decryption. Meanwhile,it has better privacy protection.
引文
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[2]张成军,刘超,郭强.关于大数据中用户信息安全保护优化仿真[J].计算机仿真,2017,34(7):154-157.
[3]刘琰,等.适用于WSN的DV签名和密钥协商机制[J].计算机工程与设计,2017,38(10):2660-2664.
[4]何云华,等.基于博弈分析的众包交通监测隐私保护机制[J].电子与信息学报,2016,38(2):340-346.
[5]王国伟,贾宗璞,彭维平.基于动态共享密钥的移动RFID双向认证协议[J].电子学报,2017,45(3):612-618.
[6]周志刚,等.面向Daa S应用的数据集成隐私保护机制研究[J].通信学报,2016,37(4):96-106.
[7]刘智慧.一种基于隐私安全保护的物联网架构体系研究[J].电子设计工程,2017,14(25):115-118.
[8]朱俊,等.面向云计算安全的无证书代理重加密方案[J].计算机工程,2017,43(8):8-14.
[9]王玲玲,马春光,刘国柱.基于位置服务的隐私保护机制度量研究综述[J].计算机应用研究,2017,34(3):647-652.
[10]彭长根,等.隐私保护的信息熵模型及其度量方法[J].软件学报,2016,27(8):1891-1903.