Privacy Preservation in Social Networks Sequential Publishing

The proliferation of social networks allowed creating a big quantity of data about users and their relationships. Such data contains much private information. Therefore, anonymization is required before publishing the data for data mining purposes (scientific research, marketing, decision support etc). Most of anonymization works focus on the privacy preserving techniques that allow publishing one instance of the social network without revealing sensitive information. However to analyze the evolution of the social network sequential releases are needed. In this paper we study the problem of privacy in sequential releases of social networks which are represented as labeled bipartite graphs to model the affiliation relationship between users and interests. We propose a solution that allows publishing sequential releases of the same social network while preserving the privacy of data. We consider a set of complex queries to study the utility given by our solution. The experiments demonstrate that the utility of data is preserved as the queries can be answered with reasonable accuracy over the anonymized data.