Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/69831
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dc.contributor.authorSang, Y.en
dc.contributor.authorShen, H.en
dc.contributor.authorTian, H.en
dc.date.issued2012en
dc.identifier.citationIEEE Transactions on Computers, 2012; 61(1):101-117en
dc.identifier.issn0018-9340en
dc.identifier.issn1557-9956en
dc.identifier.urihttp://hdl.handle.net/2440/69831-
dc.description.abstractRandom 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.en
dc.description.statementofresponsibilityYingpeng Sang, Hong Shen, and Hui Tianen
dc.language.isoenen
dc.publisherIEEE Computer Socen
dc.rights© 2012 IEEEen
dc.subjectPrivacy-preserving data mining; data perturbation; data reconstruction; underdetermined independent component analysis; Maximum A Posteriori; principal component analysis.en
dc.titleEffective reconstruction of data perturbed by random projectionsen
dc.typeJournal articleen
dc.identifier.rmid0020115183en
dc.identifier.doi10.1109/TC.2011.83en
dc.relation.granthttp://purl.org/au-research/grants/arc/DP0985063en
dc.identifier.pubid26581-
pubs.library.collectionComputer Science publicationsen
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
dc.identifier.orcidShen, H. [0000-0002-3663-6591]en
Appears in Collections:Computer Science publications

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