A Summary of $$k$$ k -Degree Anonymous Methods for Privacy-Preserving on Networks

Abstract

In recent years there has been a significant raise in the use of graph-formatted data. For instance, social and healthcare networks present relationships among users, revealing interesting and useful information for researches and other third-parties. Notice that when someone wants to publicly release this information it is necessary to preserve the privacy of users who appear in these networks. Therefore, it is essential to implement an anonymization process in the data in order to preserve users' privacy. Anonymization of graph-based data is a problem which has been widely studied last years and several anonymization methods have been developed. In this chapter we summarize some methods for privacy-preserving on networks, focusing on methods based on the kanonymity model. We also compare the results of some k-degree anonymous methods on our experimental set up, by evaluating the data utility and the information loss on real networks.

An Improved K-anonymization Approach for Preserving Graph Structural Properties

“…The authors in [25] compared the results of four algorithms, used for implementing K-degree anonymity, in terms of the information loss furthermore the data utility. These algorithms were introduced by different authors.…”

Graph Editing to a Given Neighbourhood Degree List is Fixed-Parameter Tractable

“…In complex network analysis, editing to a k-degree-anonymous graph is well-studied on various combinations of edit operations [9]. Bazgan and Nichterlein [5] proved this problem is in FPT for the parameter ∆+ when vertex and edge insertions and deletions are allowed.…”

“…The permitted edit operations are usually chosen from among vertex and edge insertions and deletions [5,22,39], but more complex operations such as edge contraction (removal of an edge and identifying the vertices it connected) [3] and edge flipping (deletion of an edge and insertion of a different edge in a graph such that the resulting graph is in the same graph class) [7] have been considered. Graph editing problems have numerous applications, which are mainly determined by the graph property: editing to an interval graph is used to correct errors in DNA sequence fragmentation [21], editing to a planar graph has found application in graph drawing [51], and editing to satisfy anonymity constraints is useful in complex network analysis [9].…”

Degree-Based Network Anonymization. (c2020)

“…The clustering-based generalization technique clusters nodes and edges into groups and anonymizes a subgraph into a super-node [16]. It can hide personal attributes and guarantees privacy.…”