Suppressing microdata to prevent classification based inference

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

The revolution of the Internet together with the progression in computer technology make it easy both for institutions to collect unprecedented amount of personal data. This pervasive data collection rally coupled with the increasing necessity of dissemination and sharing of nonaggregated data, i.e. microdata, raised a lot of concerns about privacy. One method to ensure privacy is to selectively hide the confidential1 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 [19] on microdata suppression to prevent both probabilistic and desicion 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.