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In recent years, an explosive growth of social networks has been made publicly available for understanding the behavior of users and data mining purposes. The main challenge in sharing the social network databases is protecting public released data from individual identification. The most common privacy preserving technique is anonymizing data by removing or changing some information, while the anonymized data should retain as much information as possible of the original data. K-anonymity and its extensions (e.g., L-diversity and T-closeness) have widely been used for data anonymization. The main drawback of the existing anonymity techniques is the lack of protection against attribute/link disclosure and similarity attacks. Moreover, they suffer from high amount of information loss in the released database. In order to overcome these drawbacks, this paper proposes a combined anonymizing algorithm …