Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICDE.2008.4497480
DC FieldValue
dc.titleOn the anonymization of sparse high-dimensional data
dc.contributor.authorGhinita, G.
dc.contributor.authorTao, Y.
dc.contributor.authorKalnis, P.
dc.date.accessioned2013-07-04T08:14:42Z
dc.date.available2013-07-04T08:14:42Z
dc.date.issued2008
dc.identifier.citationGhinita, G., Tao, Y., Kalnis, P. (2008). On the anonymization of sparse high-dimensional data. Proceedings - International Conference on Data Engineering : 715-724. ScholarBank@NUS Repository. https://doi.org/10.1109/ICDE.2008.4497480
dc.identifier.isbn9781424418374
dc.identifier.issn10844627
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/40889
dc.description.abstractExisting research on privacy-preserving data publishing focuses on relational data: in this context, the objective is to enforce privacy-preserving paradigms, such as kanonymity and ℓ-diversity, while minimizing the information loss incurred in the anonyrnizing process (i.e. maximize data utility). However, existing techniques adopt an indexing- or clustering-based approach, and work well for fixed-schema data, with low dimensionality. Nevertheless, certain applications require privacy-preserving publishing of transaction data (or basket data), which involves hundreds or even thousands of dimensions, rendering existing methods unusable. We propose a novel anonymization method for sparse high-dlmensional data. We employ a particular representation that captures the correlation in the underlying data, and facilitates the formation of anonymized groups with low information loss. We propose an efficient anonymization algorithm based on this representation. We show experimentally, using real-life datasets, that our method clearly outperforms existing state-of-the-art in terms of both data utility and computational overhead. © 2008 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/ICDE.2008.4497480
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1109/ICDE.2008.4497480
dc.description.sourcetitleProceedings - International Conference on Data Engineering
dc.description.page715-724
dc.identifier.isiut000257282600078
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