Privacy Preservation in Social Network Based on Anonymization Techniques

Privacy Preservation in Social Network  Based on Anonymization Techniques

Pingshui Wang1, Xuedong Zhang1, Pei Huang2


1College of Management Science and Engineering, Anhui University of Finance & Economics, Bengbu 233030, China;

2College of Computer Science and Technology, Jilin University, Changchun 130012, China

Social network data can be released for various purposes, such as statistical analysis, cluster queries, data mining and so on. However, when social network data are released, the privacy of some individuals and organizations may be disclosed. Serious concerns on privacy preservation in social networks have been raised in recent years. In this paper, in order to reduce the privacy disclosure, we propose a privacy preservation model and algorithm based on anonymization techniques for social network data, that is ( ) -anonymity algorithm. The proposed ( )-anonymity algorithm anonymize social network data to prevent privacy attacks including both content and structural information, while minimizing the anonymization cost and reducing the privacy disclosure. Extensive experiments have been conducted on synthetic data sets comparing with previous work. The result shows that the proposed anonymity algorithm could improve the security of the released social network data while maintaining data utility.