Randomizing Social Networks: a Spectrum Preserving Approach

By Xiaowei Ying, Xintao Wu. SDM, 2008

346 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/fa1fe7c39ff85fb23ce79442ad88ee946fd6f8d6

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In Artificial Intelligence

In Statistical and Nonlinear Physics

Citations in the corpus

Correlated network data publication via differential privacy (2013, background): “Some other papers [4,6,8,27,34,37] consider privacy threats due to edge disclosure, which lets an adversary learn the sensitive relationships between individuals (e.”
K-isomorphism: privacy preserving network publication against structural attacks (2010, background): “Ying and Wu [30] address the problem by edge addition/deletion and switching.”
A brief survey on anonymization techniques for privacy preserving publishing of social network data (2008, methodology): “Graph metrics can be used as background knowledge for the adversaries to breach the privacy of target individuals [12; 36].”
Preserving Link Privacy in Social Network Based Systems (2012, background): “Korolova et al. [12] show that link privacy of the overall social network can be breached even if information about the local neighborhood of social network nodes is leaked (for example, via a look-ahead feature for friend discovery).”
Resisting structural re-identification in anonymized social networks (2008, background): “Edge randomization is further explored by Ying and Wu [29], who quantify the relationship between the amount of randomization and the adversary’s ability to infer the presence of an edge.”
The Physics of Communicability in Complex Networks (2011, background): “…Estrada et al., 2009; Jungsbluth et al., 2007; Koponen and Pehkonen, 2010; Li et al., 2010; Ma et al., 2010; MacArthur et al., 2010; Malliaros and Megalooikonomou, 2011; Ren et al., 2011; Tordesillas et al., 2010; Shang, 2011b; Wang and Qin, 2010; Walker and Tordesillas, 2010; Ying and Wu, 2008).”
Relationship privacy: output perturbation for queries with joins (2009, background): “Finally, Ying and Wu [20] show that randomly perturbing the graph can provide a notion of adversarial privacy, but empirical results show the utility of the data diminishes rapidly with increased privacy.”
Differential Privacy for Deep and Federated Learning: A Survey (2022, neutral): “This approach [80] provides a higher privacy protection level and data availability compared to the approaches proposed in [107] and [108], however, the authors did not evaluate the impact of the proposed approach on the accuracy.”
K-Automorphism: A General Framework For Privacy Preserving Network Publication (2009, background): “Therefore, the problem that we want to solve in this paper is deﬁned as follows: Given an original network G , ﬁnd a network G ∗ , where We require that G is a sub-graph of G ∗ .”