Seed-Based De-Anonymizability Quantification of Social Networks


During this paper, we have a tendency to implement the primary comprehensive quantification of the right de-anonymizability and partial de-anonymizability of real-world social networks with seed info under general scenarios, which provides the theoretical foundation for the present structure-based mostly de-anonymization attacks and closes the gap between de-anonymization practice and theory. Primarily based on our quantification, we have a tendency to conduct a massive-scale analysis of the de-anonymizability of 24 real-world social networks by 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 each theoretically and experimentally, the overall structural data-based mostly de-anonymization attack can be more powerful than the seed-primarily based de-anonymization attack, and even without any seed data, a social network will be perfectly or partially de-anonymized. Finally, we have a tendency to discuss the implications of this paper. Our findings are expected to shed on analysis questions in the areas of structural information anonymization and de-anonymization and to assist knowledge homeowners evaluate their structural data vulnerability before information sharing and publishing.

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