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Graph Perturbation as Noise Graph Addition: A New Perspective for Graph Anonymization
Different types of data privacy techniques have been applied
to graphs and social networks. They have been used under different
assumptions on intruders’ knowledge. i.e., different assumptions on what
can lead to disclosure. The analysis of different methods is also led by
how data protection techniques influence the analysis of the data. i.e.,
information loss or data utility.
One of the techniques proposed for graph is graph perturbation.
Several algorithms have been proposed for this purpose. They proceed
adding or removing edges, although some also consider adding and
removing nodes.
In this paper we propose the study of these graph perturbation techniques
from a different perspective. Following the model of standard
database perturbation as noise addition, we propose to study graph perturbation
as noise graph addition. We think that changing the perspective
of graph sanitization in this direction will permit to study the properties
of perturbed graphs in a more systematic way