Suppressing microdata to prevent classification based inference

Hintoğlu, Ayça Azgın and Saygın, Yücel (2010) Suppressing microdata to prevent classification based inference. VLDB Journal, 19 (3). pp. 385-410. ISSN 1066-8888 (Print) 0949-877X (Online)

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Official URL: http://dx.doi.org/10.1007/s00778-009-0170-1


The revolution of the Internet together with the progression in computer technology makes it easy for institutions to collect an unprecedented amount of personal data. This pervasive data collection rally coupled with the increasing necessity of dissemination and sharing of non-aggregated data, i.e., microdata, raised a lot of concerns about privacy. One method to ensure privacy is to selectively hide the confidential, i.e. sensitive, information before disclosure. However, with data mining techniques, it is now possible for an adversary to predict the hidden confidential information from the disclosed data sets. In this paper, we concentrate on one such data mining technique called classification. We extend our previous work on microdata suppression to prevent both probabilistic and decision tree classification based inference. We also provide experimental results showing the effectiveness of not only the proposed methods but also the hybrid methods, i.e., methods suppressing microdata against both classification models, on real-life data sets.

Item Type:Article
Additional Information:This is the extended version of the previously published LNCS proceedings article
Uncontrolled Keywords:Privacy; Disclosure protection; Data suppression; Data perturbation; Data mining
Subjects:Q Science > Q Science (General)
ID Code:14052
Deposited By:Yücel Saygın
Deposited On:22 Jun 2010 14:32
Last Modified:25 Jul 2019 10:14

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