Towards identity anonymization on graphs

By Kun Liu, Evimaria Terzi. SIGMOD Conference, 2008

867 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/8ed89b5be84e9628b43443f3eab9ca71f8614bcb

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Citations in the corpus

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].”
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.”
Correlated network data publication via differential privacy (2013, background): “Liu and Terzi [26] propose the notion of k-degree anonymity, which requires that, for every node v, there exist at least k − 1 other nodes with the same degree as v.”
On Your Social Network De-anonymizablity: Quantification and Large Scale Evaluation with Seed Knowledge (2015, methodology): “Generally, the data anonymization techniques can be characterized into three classes: naive ID removal, k-anonymization (including randomly adding/deleting edges) [11][12], and differential privacy [13][14].”
Preserving Link Privacy in Social Network Based Systems (2012, background): “To deal with this problem, Liu and Terzi [15] propose a systematic framework for identity anonymization on graphs, where they introduce the notion of k-degree anonymity.”
Differential Privacy in Telco Big Data Platform (2015, background): “Researchers also develop different privacy protection models for other scenarios, such as the graph dataset [20, 37] (the linkage and node label information can be treated as both sensitive and quasi-identiﬁer) and continuously published data [33].”
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].”
Trustworthy AI: From Principles to Practices (2021, background): “Ways of anonymizing such data have been considered in the literature [37, 220].”
A brief survey on anonymization techniques for privacy preserving publishing of social network data (2008, methodology): “To achieve the desired degree sequence, a randomized edge swap transformation strategy was adopted in [18].”
Data and Structural k-Anonymity in Social Networks (2009, background): “Although most of the privacy work has been done for healthcare data (usually in microdata form) mainly due to the Health Insurance Portability and Accountability Act regulation [11], privacy concerns have also been raised in other fields, where data usually takes a more complex form, such as locatio”
Practical Attacks Against Graph-based Clustering (2017, background): “The first, targeted noise injection, improves on random injections [34, 54] by emulating the legitimate signal’s graph structure.”
K-Automorphism: A General Framework For Privacy Preserving Network Publication (2009, methodology): “Since the released network only contains a summary of structural information about the original network, users have to generate some random sample instances of the released network for further analysis.”
To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles (2009, neutral): “Relational data poses new challenges to preserving the privacy of individuals [3, 8, 15, 18, 19, 25].”
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.”
Knowledge Graphs (2020, neutral): “Hence a number of guarantees specific to graphs have been proposed, including k-degree anonymity [320], which ensures that individuals cannot be deanonymised by attackers with knowledge of the degree of particular individuals.”
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].”