Please use this identifier to cite or link to this item: https://doi.org/10.14778/2350229.2350255
DC FieldValue
dc.titlePublishing microdata with a robust privacy guarantee
dc.contributor.authorJianneng Cao
dc.contributor.authorKarras, P.
dc.date.accessioned2021-09-10T02:01:01Z
dc.date.available2021-09-10T02:01:01Z
dc.date.issued2012-07
dc.identifier.citationJianneng Cao, Karras, P. (2012-07). Publishing microdata with a robust privacy guarantee. Proceedings of the VLDB Endowment 5 (11) : 1388 - 1399. ScholarBank@NUS Repository. https://doi.org/10.14778/2350229.2350255
dc.identifier.issn21508097
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/200475
dc.description.abstractToday, the publication of microdata poses a privacy threat. Vast research has striven to define the privacy condition that microdata should satisfy before it is released, and devise algorithms to anonymize the data so as to achieve this condition. Yet, no method proposed to date explicitly bounds the percentage of information an adversary gains after seeing the published data for each sensitive value therein. This paper introduces β-likeness, an appropriately robust privacy model for microdata anonymization, along with two anonymization schemes designed therefor, the one based on generalization, and the other based on perturbation. Our model postulates that an adversary's confidence on the likelihood of a certain sensitive-attribute (SA) value should not increase, in relative difference terms, by more than a predefined threshold. Our techniques aim to satisfy a given β threshold with little information loss. We experimentally demonstrate that (i) our model provides an effective privacy guarantee in a way that predecessor models cannot, (ii) our generalization scheme is more effective and efficient in its task than methods adapting algorithms for the k-anonymity model, and (iii) our perturbation method outperforms a baseline approach. Moreover, we discuss in detail the resistance of our model and methods to attacks proposed in previous research. © 2012 VLDB Endowment.
dc.description.urihttps://dl.acm.org/doi/10.14778/2350229.2350255
dc.language.isoen
dc.publisherAssociation for Computing Machinery
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentCOMPUTATIONAL SCIENCE
dc.description.doi10.14778/2350229.2350255
dc.description.sourcetitleProceedings of the VLDB Endowment
dc.description.volume5
dc.description.issue11
dc.description.page1388 - 1399
dc.published.statePublished
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