一种面向高维数据挖掘的隐私保护方法

A Privacy Preservation Method for High Dimensional Data Mining

为解决隐私保护数据挖掘中的维数灾难问题,提出了一种基于随机投影技术的隐私保护方法.该方法考虑了攻击者可以通过推测随机投影矩阵重建原始数据的情况,首先提出了安全子空间和安全子空间映射的概念,然后利用通用哈希函数生成的随机投影矩阵构造了一个安全子空间映射,实现低失真嵌入的同时保证了数据的安全,最后证明了安全子空间能够保护原始数据间的欧式距离和内积.实验结果表明,在保护数据隐私的前提下,该方法能够有效的保证数据挖掘应用中的数据质量.

This paper proposes a privacy preservation method based on random projection to overcome the curse of dimen-sionality in privacy preserving data mining .To prevent leaks of random matrix which can lead to the reconstruction attack ,it first proposes the concepts of secure subspace and secure subspace mapping .Then ,it constructs a secure subspace mapping using hash technique ,which is implemented by a random projection matrix ,and it achieves a low distortion embedding while preserving the data privacy .Finally ,it proves that the secure subspace can preserve the Euclidean distance and inner product between any two original points .The experimental results show that the proposed technique can ensure the data quality in different data mining applications ef-fectively under the precondition of preserving data privacy .