A Practical Attack to De-anonymize Social Network Users

By Gilbert Wondracek, Thorsten Holz, Engin Kirda, Christopher Krügel. IEEE Symposium on Security and Privacy, 2010

403 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/08c2ba4d7183d671b9a7652256de17110b81c723

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Citations in the corpus

"On the internet, nobody knows you're a dog": a twitter case study of anonymity in social networks (2014, methodology): “These non-sensitive interest disclosures can potentially be used to deanonymize the Anonymous users using techniques similar to [45] (which shows that a user’s group membership sets in a social network are generally unique and can be used to identify the user).”
On Your Social Network De-anonymizablity: Quantification and Large Scale Evaluation with Seed Knowledge (2015, background): “As we discussed in Section IV, for i ∈ A, besides the structural connection to the users in S, the structural information between i and other users in A is also helpful to improve the de-anonymization performance (as shown in Theorem 3).”
Fawkes: Protecting Privacy against Unauthorized Deep Learning Models (2020, background): “Even so, a third party might be able to restore those labels and re-identify her in those photos using friendlist intersection attacks [62].”
You are what you like! Information leakage through users' Interests (2012, background): “Computing the neighbors of each user in the feature space (i.e., whose IFVs are similar in the feature space) to discover similar users, and exploiting this neighborhood to infer hidden attributes.”
Privacy for All: Ensuring Fair and Equitable Privacy Protections (2018, background): “Wondracek et al. (2010) propose a deanonymization attack based the group membership information on social networks.”
On the Feasibility of Internet-Scale Author Identification (2012, background): “Regardless of future developments, our work has implications for the authors of all the sensitive anonymous speech that already exists on the Web and in various databases around the world.”
Structural Data De-anonymization: Quantification, Practice, and Implications (2014, background): “Several successful DA attacks on structural data were proposed recently, including structure based schemes [1][2][3] and semantics based schemes [4].”