关键词: 隐私保护, 微分流形, 等距变换, 拉普拉斯特征映射

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.

Key words: privacy preserving, differentiable manifold, isometric transformation, Laplacian eigenmap