Suppressing microdata to prevent classification ba

Authors: Ayça Azgin Hintoglu Yücel Saygın

Publish Date: 2009/11/19

Volume: 19, Issue: 3, Pages: 385-410

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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 nonaggregated data ie microdata raised a lot of concerns about privacy One method to ensure privacy is to selectively hide the confidential ie 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 ie methods suppressing microdata against both classification models on reallife data sets

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