图 书 馆
混合矩阵已知时随机投影扰动数据的重建Reconstructing Data Perturbed by Random Projections when the Mixing Matrix is Known |
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| 课程网址: | http://videolectures.net/ecmlpkdd09_sang_rdprpmmk/ |
| 主讲教师: | Yingpeng Sang |
| 开课单位: | 阿德莱德大学 |
| 开课时间: | 2009-10-20 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Random Projection (RP) has drawn great interest from the research of privacy-preserving data mining due to its high efficiency and security. It was proposed in \cite{Liu} where the original data set composed of $m$ attributes, is multiplied with a mixing matrix of dimensions $k\times m ~(m>k)$ which is random and orthogonal on expectation, and then the $k$ series of perturbed data are released for mining purposes. To our knowledge little work has been done from the view of the attacker, to reconstruct the original data to get some sensitive information, given the data perturbed by RP and some priori knowledge, e.g. the mixing matrix, the means and variances of the original data. In the case that the attributes of the original data are mutually independent and sparse, the reconstruction can be treated as a problem of Underdetermined Independent Component Analysis (UICA), but UICA has some permutation and scaling ambiguities. In this paper we propose a reconstruction framework based on UICA and also some techniques to reduce the ambiguities. The cases that the attributes of the original data are correlated and not sparse are also common in data mining. We also propose a reconstruction method for the typical case of Multivariate Gaussian Distribution, based on the method of Maximum A Posterior (MAP). Our experiments show that our reconstructions can achieve high recovery rates, and outperform the reconstructions based on Principle Component Analysis (PCA). |
| 关 键 词: | 随机投影; 隐私保护; 数据挖掘 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-03-27：lxf |
| 阅读次数: | 61 |