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dc.contributor.authorLuo, Changqing
dc.contributor.authorSalinas Monroy, Sergio A.
dc.contributor.authorLi, Pan
dc.date.accessioned2019-04-10T20:23:49Z
dc.date.available2019-04-10T20:23:49Z
dc.date.issued2018-12
dc.identifier.citationC. Luo, S. Salinas and P. Li, "Efficient Privacy-Preserving Large-Scale CP Tensor Decompositions," 2018 IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, United Arab Emirates, 2018, pp. 1-6en_US
dc.identifier.isbn978-153864727-1
dc.identifier.urihttps://doi.org/10.1109/GLOCOM.2018.8647978
dc.identifier.urihttp://hdl.handle.net/10057/15997
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractTensor decompositions are very powerful tools for analyzing multi-dimensional multi-modal data. Particularly, CP tensor decomposition is one of the most fundamental tensor decomposition models. However, it is usually computationally expensive to conduct CP tensor decompositions on a large-scale tensor by common algorithms like alternative least squares (ALS). To address this issue, one widely recognized solution is to adopt cloud computing. However, this raises privacy concerns due to the private information carried by a tensor. Previous algorithms for privacy-preserving outsourcing of tensor decompositions and other related computations require heavy communication cost. In this paper, we first develop an efficient tensor transformation scheme to protect the private information carried by elements' values of a tensor. Then we design a privacy-preserving outsourcing algorithm for ALS based CP tensor decompositions. We implement our proposed algorithm on a laptop and Amazon EC2 cloud and offer experiment results to show the sianificant computing time-savings.en_US
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries2018 IEEE Global Communications Conference (GLOBECOM);
dc.subjectBig data analysisen_US
dc.subjectData privacyen_US
dc.subjectTensor decompositionen_US
dc.titleEfficient privacy-preserving large-scale CP tensor decompositionsen_US
dc.typeConference paperen_US
dc.rights.holder© 2018, IEEEen_US
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