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k-Degree Anonymity And Edge Selection: Improving Data Utility In Large Networks
Faculty of Computer Science, Multimedia and Telecommunications, Internet Interdisciplinary Institute (IN3), Universitat Oberta de Catalunya (UOC), Barcelona, Spain.
Department of Information and Communications Engineering, Universitat Autònoma de Barcelona (UAB), Bellaterra, Spain.
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0002-0368-8037
2017 (English)In: Knowledge and Information Systems, ISSN 0219-1377, E-ISSN 0219-3116, Vol. 50, no 2, p. 447-474Article in journal (Refereed) Published
Abstract [en]

The problem of anonymization in large networks and the utility of released data are considered in this paper. Although there are some anonymization methods for networks, most of them cannot be applied in large networks because of their complexity. In this paper, we devise a simple and efficient algorithm for k-degree anonymity in large networks. Our algorithm constructs a k-degree anonymous network by the minimum number of edge modifications. We compare our algorithm with other well-known k-degree anonymous algorithms and demonstrate that information loss in real networks is lowered. Moreover, we consider the edge relevance in order to improve the data utility on anonymized networks. By considering the neighbourhood centrality score of each edge, we preserve the most important edges of the network, reducing the information loss and increasing the data utility. An evaluation of clustering processes is performed on our algorithm, proving that edge neighbourhood centrality increases data utility. Lastly, we apply our algorithm to different large real datasets and demonstrate their efficiency and practical utility.

Place, publisher, year, edition, pages
2017. Vol. 50, no 2, p. 447-474
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science; INF303 Information Security
Identifiers
URN: urn:nbn:se:his:diva-13356DOI: 10.1007/s10115-016-0947-7ISI: 000393661500004Scopus ID: 2-s2.0-85010032093OAI: oai:DiVA.org:his-13356DiVA, id: diva2:1071372
Available from: 2017-02-04 Created: 2017-02-04 Last updated: 2018-06-11Bibliographically approved

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Torra, Vicenç

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CiteExportLink to record
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Citation style
  • apa
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  • Other locale
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Output format
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