A De-anonymization Attack for Social Network Graph Based on Structural and Node Feature Similarity

QIN CHEN, ZHAOYONG WANG, MIN ZHANG, HONGLIN ZHU, SHIJIE FENG

Abstract


With the wide usage of Internet, social network has become an important carrier of information publishing and transmission in contemporary society. However, the anonymized information can still be de-anonymized because of the high frequency data-sharing in social network, which causes the leakage of users’ privacy information. This paper proposes a novel de-anonymization method based on the structure of the social network graph of users and the characteristics of user nodes to disclose users’ private information. It utilizes the similarity difference between the structural features of the social network graph and the characteristics of the node attributes to realize the mapping between nodes, so as to successfully de-anonymize the users of anonymous datasets. Experiments on two real datasets show that our anonymization method outperforms in accuracy and execution time.

Keywords


Social Network; Privacy; Similarity; De-anonymization.


DOI
10.12783/dtcse/iceiti2017/18923

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