计算机工程与应用2011，Vol.47Issue(8)：79-82,248,5.DOI:10.3778/j.issn.1002-8331.2011.08.024
流形学习及维数约简在数据隐私保护中的应用
Application of manifold learning and nonlinear dimensionality reduction in private preserving
向婷婷 1罗运纶 2王学松1
作者信息
- 1. 北京师范大学信息科学与技术学院,北京,100875
- 2. 北京师范大学珠海分校信息技术学院,广东,珠海,519085
- 折叠
摘要
Abstract
To deal with the privacy preserving problem during data mining for sensitive black points in traffic accidents,this paper presents a new method,which is based on the isometric transformation and the differential manifold,to improve the privacy preserving level of the original data. It disturbs the data by doing isometric transformation based on rotation.The nonlinear dimensionality reduction is done to high-dimensional data with Laplacian Eigenmap to further disturb the data,while preserving the inner structure of the data at the same time. This method is effectively applied to the privacy preserving problem during data mining for sensitive black points in traffic accidents, while reducing the dimensionality of the original data for later data mining and data analyzing.关键词
隐私保护/微分流形/等距变换/拉普拉斯特征映射Key words
privacy preserving/ differentiable manifold/ isometric transformation/ Laplacian eigenmap分类
信息技术与安全科学引用本文复制引用
向婷婷,罗运纶,王学松..流形学习及维数约简在数据隐私保护中的应用[J].计算机工程与应用,2011,47(8):79-82,248,5.
