NDLI: Suppressing microdata to prevent classification based inference

Content Provider	Springer Nature Link
Author	Azgin Hintoglu, Ayça Saygın, Yücel
Copyright Year	2009
Abstract	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.
Starting Page	385
Ending Page	410
Page Count	26
File Format	PDF
ISSN	10668888
Journal	The VLDB Journal
Volume Number	19
Issue Number	3
e-ISSN	0949877X
Language	English
Publisher	Springer-Verlag
Publisher Date	2009-11-19
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Privacy Disclosure protection Data suppression Data perturbation Data mining Database Management
Content Type	Text
Resource Type	Article
Subject	Information Systems Hardware and Architecture

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