Abstract
Social networks have a great influence in business, for which the data of social networks are usually released for research purpose. Since the published data might contain sensitive information of users, the identities of which are removed for anonymity before release. However, adversaries could still utilize some background knowledge to re-identify users. In this paper, we propose a novel attack model named edge-neighborhood graph attack (ENGA) against anonymized social networks, in which adversaries are assumed to have background knowledge about targets and their two-hop neighbors represented by 1-neighborhood graph and \(1^{*}\)-neighborhood graphs respectively. Based on such model, a de-anonymous approach is proposed to re-identify users in anonymous social networks. Theoretical analysis indicate that ENGA has a higher de-anomymization rate. And experiments conducted on synthetic data sets and real data sets illustrate the effectiveness of ENGA.
This work is supported by the National Natural Science Foundation of China (Grant No. 61771140, No. U1405255, No. U1905211, No. 61702100, No. 171061).
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Zhang, H., Xu, L., Lin, L., Wang, X. (2020). De-anonymizing Social Networks with Edge-Neighborhood Graph Attacks. In: Yu, S., Mueller, P., Qian, J. (eds) Security and Privacy in Digital Economy. SPDE 2020. Communications in Computer and Information Science, vol 1268. Springer, Singapore. https://doi.org/10.1007/978-981-15-9129-7_49
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DOI: https://doi.org/10.1007/978-981-15-9129-7_49
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