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Graph Perturbation as Noise Graph Addition: A New Perspective for Graph Anonymization
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. Hamilton Institute, Maynooth University, Ireland. (Skövde Artificial Intelligence Lab)ORCID iD: 0000-0002-0368-8037
CYBERCAT-Center for Cybersecurity Research of Catalonia, Internet Interdisciplinary Institute (IN3), Universitat Oberta de Catalunya, Barcelona, Spain.
2019 (English)In: Data Privacy Management, Cryptocurrencies and Blockchain Technology: ESORICS 2019 International Workshops, DPM 2019 and CBT 2019, Luxembourg, September 26–27, 2019, Proceedings / [ed] Cristina Pérez-Solà, Guillermo Navarro-Arribas, Alex Biryukov, Joaquin Garcia-Alfaro, Cham: Springer, 2019, p. 121-137Conference paper, Published paper (Refereed)
Abstract [en]

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. 

Place, publisher, year, edition, pages
Cham: Springer, 2019. p. 121-137
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11737
Keywords [en]
Data privacy, Edge removal, Graphs, Noise addition, Social networks, Blockchain, Computer privacy, Electronic money, Perturbation techniques, Social networking (online), Anonymization, Data protection techniques, Data utilities, Information loss, Sanitization
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
URN: urn:nbn:se:his:diva-18009DOI: 10.1007/978-3-030-31500-9_8Scopus ID: 2-s2.0-85075616311ISBN: 978-3-030-31499-6 (print)ISBN: 978-3-030-31500-9 (electronic)OAI: oai:DiVA.org:his-18009DiVA, id: diva2:1377777
Conference
ESORICS 2019 International Workshops, DPM 2019 and CBT 2019, Luxembourg, September 26–27, 2019
Note

Also part of the Security and Cryptology book sub series (LNSC, volume 11737)

Available from: 2019-12-12 Created: 2019-12-12 Last updated: 2019-12-16Bibliographically approved

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