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- W1993077546 abstract "As a severe threat in anonymized data publication, proximity breach is gaining increasing attention. Such breach occurs when an attacker learns with high confidence that the sensitive information of a victim associates with a set of semantically proximate values, even though not sure about the exact one. Recently (¿, ¿)-dissimilarity [14] has been proposed as an effective countermeasure against general proximity attack. In this paper, we present a detailed analytical study on the fulfillment of this principle, derive criteria to efficiently test its satisfiability for given microdata, and point to a novel anonymization model, XCOLOR, with theoretical guarantees on both operation efficiency and utility preservation." @default.
- W1993077546 created "2016-06-24" @default.
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- W1993077546 date "2010-01-01" @default.
- W1993077546 modified "2023-09-23" @default.
- W1993077546 title "XColor: Protecting general proximity privacy" @default.
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- W1993077546 doi "https://doi.org/10.1109/icde.2010.5447910" @default.
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