A Novel k-Anonymization Approach to Prevent Insider Attack in Collaborative Social Network Data Publishing

Social network data analysts can retrieve improved results if mining operations are performed on collaborative social network data instead of independent social network data. The collaborative social network can be constructed by joining data of all social networking sites. This data may contain sensitive information about individuals in its original form and sharing of such data, as it is, may violate individual privacy. Hence, various techniques are discussed in literature for privacy preserving publishing of social network data. However, these techniques suffer from the insider attack, performed by colluding data provider(s) to breach the privacy of the social network data contributed by other data providers. In this paper, we propose an approach that offers protection against the insider attack in the collaborative social network data publishing scenario. Experimental results demonstrate that our approach preserves data utility while protecting collaborated social network data against the insider attack.