基于梯度采样的大数据交叉访问授权技术仿真

如何对数据进行授权管理,确保数据的机密性等至关重要。为此提出了一种基于梯度采样的大数据交叉访问授权方法。采用多项式变换技术,将数据转换为可直接使用双率数据进行采样的数学模型,推导出估计损失数据的梯度随机算法,并且在算法中加入遗忘因子实现数据的梯度采样。用户在进行数据访问前需要进行初始数据加密,然后在将其转交至重加密服务器进行处理。同时,进行代理重加密密钥的约束,分割重加密密钥,其中一部分交由云端代理重加密服务器进行处理,另一部分则为创建者所控制,实现大数据的交叉访问授权。仿真结果表明,所提方法相比传统方法性能有了一定程度的提高。

Simulation of Big Data Cross-Access Authorization Technology Based on Gradient Sampling

In this article,a big data cross-access authorization method based on gradient sampling was put forward.At first,the polynomial transformation technique was used to transform data into a mathematical model which could be sampled directly by dual-rate data,so that the gradient random algorithm for estimating loss data was derived.And then,the forgetting factor was added to the algorithm to realize the gradient sampling of data.In addition,the user needed to encrypt the initial data before accessing the data,and then transferred it to the re-encryption server.At the same time,the agent re-encryption key was constrained and the re-encryption key was separated.Some of them were processed by the cloud agent re-encryption server,and the other part was controlled by the creator.Thus,the big data cross-access authorization was realized.Simulation results show that the proposed method has better performance than the traditional method.