A Privacy Preserving Method for Publishing Set-valued Data and Its Correlative Social Network

Set-valued data and social network provide opportunities to mine useful, yet potentially security-sensitive, information. While there are mechanisms to anonymize data and protect the privacy separately in set-valued data and in social network, the existing approaches in data privacy do not address the privacy issue which emerge when publishing set-valued data and its correlative social network simultaneously. In this paper, we propose a privacy attack model based on linking the set-valued data and the social network topology information and a novel technique to defend against such attack to protect the individual privacy. To improve data utility and the practicality of our scheme, we use local generalization and partial suppression to make set-valued data satisfy the grouped ρ-uncertainty model and to reduce the impact on the community structure of the social network when anonymizing the social network. Experiments on real-life data sets show that our method outperforms the existing mechanisms in data privacy and, more specifically, that it provides greater data utility while having less impact on the community structure of social networks.