A probabilistic inference attack on suppressed social networks

Official URL: http://192.168.1.20/record=b1378283 (Table of Contents)

Social Networks (SNs) are now widely used by modern time internet users to share any personal information. Such networks are so rich in information content that there is public and commercial benefit in sharing them with other third parties. However, information stored in SNs are mostly person specific and subject to privacy concerns. One way to address the privacy issues is to give the control of the data to the users enabling them to suppress data that they choose not to share with third parties. Unfortunately, above mentioned preference-based suppression techniques are not sufficient to protect privacy mainly because they do not allow users to control data about other users they are linked with Information about neighbors becomes an inference channel in an SN when there is known correlation between the existence of a link between two users and the users having the same sensitive information. In this thesis, we propose a probabilistic inference attack on a suppressed social network data, that can successfully predict a suppressed label by looking at neighboring users' data. The attack algorithm is designed for a realistic adversary that knows, from background or external sources, the correlations between labels and links in the SN. We experimentally show that it is possible to recover majority of the suppressed labels of users even in a highly suppressed SN.