Reducing Attribute Couplet Attack in Social Networks using Factor Analysis

摘要

Social Network is an emerging platform where a huge data are available without hiding individuals or groups of people's sensitive information on the network. This highly sensitive data can be used by a third party or an external attacker for social targeting where the privacy is not guaranteed. Anonymizing data is one of the privacy-preserving models which help in reducing privacy leaks and also it increases the data utility. However, the attackers try to derive new methods to identify the individual's data. One among the new method is attributed couplet attack with some background knowledge to find the identity of the individuals by using a pair of node attributes. In the existing approach, the k-couplet anonymity achieves the privacy under the attack called attribute couplet. In this paper, we use factor analysis to reduce this attribute couplet attack.