Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/69831
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Type: Journal article
Title: Effective reconstruction of data perturbed by random projections
Author: Sang, Y.
Shen, H.
Tian, H.
Citation: IEEE Transactions on Computers, 2012; 61(1):101-117
Publisher: IEEE Computer Soc
Issue Date: 2012
ISSN: 0018-9340
1557-9956
Statement of
Responsibility: 
Yingpeng Sang, Hong Shen, and Hui Tian
Abstract: Random Projection (RP) has raised great concern among the research community of privacy-preserving data mining, due to its high efficiency and utility, e.g., keeping the euclidean distances among the data points. It was shown in [33] that, if the original data set composed of m attributes is multiplied by a mixing matrix of ktimes m (m>;k) which is random and orthogonal on expectation, then the k series of perturbed data can be released for mining purposes. Given the data perturbed by RP and some necessary prior knowledge, to our knowledge, little work has been done in reconstructing the original data to recover some sensitive information. In this paper, we choose several typical scenarios in data mining with different assumptions on prior knowledge. For the cases that an attacker has full or zero knowledge of the mixing matrix R, respectively, we propose reconstruction methods based on Underdetermined Independent Component Analysis (UICA) if the attributes of the original data are mutually independent and sparse, and propose reconstruction methods based on Maximum A Posteriori (MAP) if the attributes of the original data are correlated and nonsparse. Simulation results show that our reconstructions achieve high recovery rates, and outperform the reconstructions based on Principal Component Analysis (PCA). Successful reconstructions essentially mean the leakage of privacy, so our work identify the possible risks of RP when it is used for data perturbations.
Keywords: Privacy-preserving data mining; data perturbation; data reconstruction; underdetermined independent component analysis; Maximum A Posteriori; principal component analysis.
Rights: © 2012 IEEE
RMID: 0020115183
DOI: 10.1109/TC.2011.83
Grant ID: http://purl.org/au-research/grants/arc/DP0985063
Appears in Collections:Computer Science publications

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