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Recently, social network services are rapidly growing and it is estimated that this trend will continue in the future. Social network data can be published for various purposes such as statistical analysis and population studies. When data publication, however, it may disclose the personal privacy of some people, since it can be combined with external information. Therefore, a social network data holder has to remove the identifiers of persons and modify data which have the potential to disclose the privacy of the persons by combining it with external information. The utility of data is maximized when the modification of data is minimized. In this paper, we propose a privacy protection method for social network data that considers both structural and content information. Previous work did not consider content information in the social network or distorted too much structural information. We also verify the effectiveness and applicability of the proposed method under various experimental conditions.

A Privacy Protection Method in Social Networks Considering Structure and Content Information