Anonymization of Sensitive Quasi-Identifiers for l-Diversity and t-Closeness
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DOI: 10.1109/tdsc.2017.2698472
Abstract: A number of studies on privacy-preserving data mining have been proposed. Most of them assume that they can separate quasi-identifiers (QIDs) from sensitive attributes. For instance, they assume that address, job, and age are QIDs… read more here.
Keywords: quasi identifiers; diversity closeness; anonymization; sensitive attributes ... See more keywords