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  • 1.
    Casas-Roma, Jordi
    et al.
    Faculty of Computer Science, Multimedia and Telecommunications, Internet Interdisciplinary Institute (IN3), Universitat Oberta de Catalunya (UOC), Barcelona, Spain.
    Herrera-Joancomarti, Jordi
    Department of Information and Communications Engineering, Universitat Autònoma de Barcelona (UAB), Bellaterra, Spain.
    Torra, Vicenç
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    k-Degree Anonymity And Edge Selection: Improving Data Utility In Large Networks2017In: Knowledge and Information Systems, ISSN 0219-1377, E-ISSN 0219-3116, Vol. 50, no 2, p. 447-474Article in journal (Refereed)
    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.

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