Parzen窗核密度估计的模式分类隐私保护方法

The use of pattern classification for preserving privacy based on Parzen window kernel density estimation

针对大规模数据集上的模式分类任务,提出了一种基于Parzen窗核密度估计的模式分类隐私保护算法。该算法首先利用Parzen窗算法对原始大规模训练集服从的概率密度进行估计,然后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该算法的有效性。

In this paper,we proposed a pattern classification privacy preserve algorithm based on Parzen window kernel density estimation on large scale dataset.Firstly,the probability density followed by the original large scale training set is estimated.Then we can construct replacement training samples by the estimated probability.Finally,the replacement training samples are published for 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.