Torra, Vicenç and Salas, Julián
(2019)
Graph Perturbation as Noise Graph Addition: A New Perspective for Graph Anonymization.
In:
Data Privacy Management, Cryptocurrencies and Blockchain Technology.
Lecture Notes in Computer Science book series (LNCS)
(11737).
Springer, pp. 121-137.
ISBN 9783030314996
Abstract
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.
| Item Type: |
Book Section
|
| Additional Information: |
Cite as: Torra V., Salas J. (2019) Graph Perturbation as Noise Graph Addition: A New Perspective for Graph Anonymization. In: Pérez-Solà C., Navarro-Arribas G., Biryukov A., Garcia-Alfaro J. (eds) Data Privacy Management, Cryptocurrencies and Blockchain Technology. DPM 2019, CBT 2019. Lecture Notes in Computer Science, vol 11737. Springer, Cham. https://doi.org/10.1007/978-3-030-31500-9_8 . This chapter is licensed under the terms of the Creative Commons
Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/),
which permits use, sharing, adaptation, distribution and reproduction in any medium
or format, as long as you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons license and indicate if changes were
made.
The images or other third party material in this chapter are included in the
chapter’s Creative Commons license, unless indicated otherwise in a credit line to the
material. If material is not included in the chapter’s Creative Commons license and
your intended use is not permitted by statutory regulation or exceeds the permitted
use, you will need to obtain permission directly from the copyright holder. |
| Keywords: |
Data privacy; Graphs; Social networks; Noise addition;
Edge removal; |
| Academic Unit: |
Faculty of Science and Engineering > Research Institutes > Hamilton Institute |
| Item ID: |
14384 |
| Identification Number: |
https://doi.org/10.1007/978-3-030-31500-9_8 |
| Depositing User: |
Vicenç Torra
|
| Date Deposited: |
27 Apr 2021 14:24 |
| Publisher: |
Springer |
| Refereed: |
Yes |
| URI: |
|
| Use Licence: |
This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available
here |
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