Preserving the Privacy of Sensitive Relationships in Graph Data

By Elena Zheleva, Lise Getoor. PinKDD, 2007

452 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/a5e1ea32391b75b5d467fc4d2a58a539f4834a33

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In Artificial Intelligence

In Management Science and Operations Research

In Sociology and Political Science

Citations in the corpus

Location Privacy Protection Based on Differential Privacy Strategy for Big Data in Industrial Internet of Things (2018, methodology): “[9] proposed the k-anonymity method and also a large number of methods based on k-anonymity [10]–[12].”
K-isomorphism: privacy preserving network publication against structural attacks (2010, background): “[31] E. Zheleva and L. Getoor.”
A taxonomy of privacy-preserving record linkage techniques (2013, background): “Only limited work has been conducted that investigates the application of advanced classiﬁcation techniques that have been developed for record linkage in the past decade, such as machine learning or graph-based collective classiﬁcation approaches, in a privacy-preserving context [109,140,141].”
Preserving Link Privacy in Social Network Based Systems (2012, background): “The second class of approaches aim to introduce perturbation in the social graph by adding and deleting edges and vertices.”
A brief survey on anonymization techniques for privacy preserving publishing of social network data (2008, methodology): “[37] E. Zheleva and L. Getoor.”
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].”
Protecting privacy when mining and sharing user data (2012, background): “In [197] the emphasis is on protecting the types of links associated with individuals in an anonymized release.”
Data and Structural k-Anonymity in Social Networks (2009, methodology): “The comparison we present between the SaNGreeA algorithm and the Zheleva’s algorithm [31] is made with respect to the quality of the results they produce, measured against the normalized generalization information loss and the normalized structural information loss.”
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection (2023, background): “(nodes), but also their relationship (edges) within the time series data [293], [301].”
To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles (2009, unknown): “For example, in graph data, there is a third type of disclosure attack: link re-identification [25].”
Social Network Analysis and Mining for Business Applications (2011, methodology): “2008; Zheleva and Getoor 2007] do not alter the graph data like the methods of the two previous categories; instead, they group together nodes into supernodes of size at least k, where k is the required threshold of anonymity, and then publish the graph data in that coarse resolution.”