Preserving Privacy in Social Networks Against Neighborhood Attacks

By Bin Zhou, Jian Pei. IEEE International Conference on Data Engineering, 2008

791 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications

View Paper: https://www.semanticscholar.org/paper/a8299acc57d87c852e72f68ab6e069114a0c0579

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Citations in the corpus

Preserving Link Privacy in Social Network Based Systems (2012, background): “Another notion of graph anonymity in social networks is presented by Pei and Zhou [38]: A graph is k-anonymous if for every node there exist at least k−1 other nodes that share isomorphic neighborhoods.”
Correlated network data publication via differential privacy (2013, background): “Zhou and Pei [39] demand that any vertex cannot be reidentified in an anonymized graph with probability greater than 1k by an adversary equipped with 1-neighborhood background knowledge.”
Towards a Visual Privacy Advisor: Understanding and Predicting Privacy Risks in Images (2017, neutral): “Other tasks include preserving one’s privacy while using social networks [15, 52, 27] and exploring privacy settings [11, 8, 28].”
Accurate Estimation of the Degree Distribution of Private Networks (2009, methodology): “Empirically, it appears that with increasing anonymity, the graph is rapidly distorted and some metrics are systematically biased [3], [7], [13], [22], [23].”
A Framework for Computing the Privacy Scores of Users in Online Social Networks (2009, background): “, [1], [3], [4], [7], [9]) mainly focuses on corporate-scale privacy concerns, i.”
Characterizing privacy in online social networks (2008, background): “There is a growing amount of literature [2, 6, 17] on OSNs as well, although the focus is on examining identity leakage due to”
A brief survey on anonymization techniques for privacy preserving publishing of social network data (2008, methodology): “Zhou and Pei [39] introduced a practical greedy method to anonymize a social network to satisfy the k-anonymity requirement in two steps.”
Resisting structural re-identification in anonymized social networks (2008, background): “It is not well-understood how publishing a network threatens privacy; initial investigations, including anonymization algorithms, are just emerging [4, 14, 17, 24, 26, 29, 30, 31].”
Data and Structural k-Anonymity in Social Networks (2009, methodology): “Zhou and Pei have a similar social network model, they consider the nodes to be labeled (having one attribute, which can be seen as a quasi-identifier) and that only the near vicinity (1-radius neighborhood) of some target individuals is completely known to an intruder [32].”
Measuring Large-Scale Social Networks with High Resolution (2014, background): “Specific attacks are also possible in network data, as nodes can be identified based on the network structure and attributes of the neighbors [76,77].”
K-isomorphism: privacy preserving network publication against structural attacks (2010, background): “Recently, several methods [32, 19] have been proposed to achieve k-anonymity [23, 22, 24] based on various adversary knowledge.”
Protecting privacy when mining and sharing user data (2012, background): “[198] and [124] pursue the question of privacy as it relates to social networks from various other perspectives.”
K-Automorphism: A General Framework For Privacy Preserving Network Publication (2009, methodology): “Obviously, if G is a k -automorphic network, for any vertex v in G , we cannot distinguish v from its k − 1 symmetric vertices based on any structural information.”
Boosting the accuracy of differentially private histograms through consistency (2009, background): “Most techniques [7, 8, 13] publish transformed graphs designed to protect anonymity; we are not aware of any that satisfy differential privacy.”
Towards identity anonymization on graphs (2008, background): “difference in the definition, the corresponding algorithmic problems arising in [14] are also different from the problems we consider in this paper.”
Relationship privacy: output perturbation for queries with joins (2009, background): “Each publishes a transformed graph that provides anonymity by creating structurally similar groups [11, 22, 12, 3].”
Link prediction by de-anonymization: How We Won the Kaggle Social Network Challenge (2011, background): “Several papers have claimed to provide k-anonymity, or variants thereof, for graphs [16], [20], [9], [30].”