吉林大学学报(理学版)

• 计算机科学 • 上一篇    下一篇

一种新的高维稠密数据隐私保护算法

朱献文, 孙伟   

  1. 黄淮学院 国际教育学院, 河南 驻马店 463000
  • 收稿日期:2016-05-27 出版日期:2017-07-26 发布日期:2017-07-13
  • 通讯作者: 朱献文 E-mail:zhu.x.w@163.com

A New Privacy Preserving Algorithm for High Dimensional Dense Data

ZHU Xianwen, SUN Wei   

  1. College of International, Huanghuai University, Zhumadian 463000, Henan Province, China
  • Received:2016-05-27 Online:2017-07-26 Published:2017-07-13
  • Contact: ZHU Xianwen E-mail:zhu.x.w@163.com

摘要: 针对隐私保护数据挖掘中的维数灾难问题, 提出一种基于随机投影技术的隐私保护算法. 该算法通过定义l投影扰动和PreventΩ数据集的概念, 构造一种根据投影维数的不同, 投影矩阵的稀疏度也相应变化的稀疏投影数据扰动, 增加了数据的安全性. 实验结果表明, 在保护数据隐私的前提下 , 该算法能有效保证数据挖掘应用中的数据质量.

关键词: l投影, Prevent&Omega, 高维稠密数据, 数据集, 数据隐私保护

Abstract: Aiming at the problem of the curse of dimensionality in privacy preserving data mining, we proposed a privacy preserving algorithm based on the technique of random projection. We defined the concept of l projection perturbation and PreventΩ data set, and constructed a sparse projection data perturbation based on projection dimension of different projection matrix sparsity corresponding changes, which increased the security of data. The experimental results show that this method can effectively guarantee the quality of data in data mining applications under the premise of protecting data privacy.

Key words: Prevent-Ω data set, high dimensional dense data;l projection, data privacy protection

中图分类号: 

  • TP391