Parzen窗核密度估计的大规模数据模式分类隐私保护方法

A Pattern Classification Privacy Preservation Algorithm Based on Parzen Window Kernel Density Estimation for Large Data Set

针对大规模数据集上的模式分类任务,提出基于Parzen窗核密度估计的模式分类隐私保护算法。利用Parzen窗算法对原始大规模训练集服从的概率密度进行估计,根据估计的概率密度函数构造la个替换训练样本,其中l为原始样本的数目,a通过10折交叉验证方式确定。最后发布替换训练样本进行模式分类,以实现原始数据上的隐私保护。在Adult数据集上的仿真实验充分验证了算法的有效性。

In this paper, a pattern classification privacy preservation algorithm is proposed based on the Parzen window kernel density estimation on large scale dataset. Firstly, the probability density is estimated through the original large scale training set. Then the replacement training samples are constructed by the estimated probability. Finally, the replacement training samples are published for the pattern classification training. Thus the privacy on the original training set can be protected effectively. The simulation experiments on Adult datasets fully verify the effectiveness of the proposed algorithm.