Seed-Based De-Anonymizability Quantification of Social Networks

Shouling Ji, Weiqing Li, Neil Zhenqiang Gong, Prateek Mittal, Raheem Beyah
2016 IEEE Transactions on Information Forensics and Security  
In this paper, we implement the first comprehensive quantification of the perfect de-anonymizability and partial de-anonymizability of real-world social networks with seed information under general scenarios, which provides the theoretical foundation for the existing structure-based de-anonymization attacks and closes the gap between de-anonymization practice and theory. Based on our quantification, we conduct a large-scale evaluation of the de-anonymizability of 24 real-world social networks
more » ... quantitatively showing the conditions for perfectly and partially de-anonymizing a social network, how de-anonymizable a social network is, and how many users of a social network can be successfully de-anonymized. Furthermore, we show that both theoretically and experimentally, the overall structural information-based de-anonymization attack can be more powerful than the seed-based de-anonymization attack, and even without any seed information, a social network can be perfectly or partially de-anonymized. Finally, we discuss the implications of this paper. Our findings are expected to shed on
doi:10.1109/tifs.2016.2529591 fatcat:qlavnsptjbbhphlabjwii7mqwi