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  • 1.
    Stokes, Klara
    et al.
    Universitat Rovira i Virgili, Tarragona, Catalonia, Spain.
    Torra, Vicenç
    IIIA, Institut d’Investigació en Intel ligència Artificial CSIC, Consejo Superior de Investigaciones Científicas, Bellaterra, Catalonia, Spain.
    Reidentification and k-anonymity: a model for disclosure risk in graphs2012In: Soft Computing - A Fusion of Foundations, Methodologies and Applications, ISSN 1432-7643, E-ISSN 1433-7479, Vol. 16, no 10, p. 1657-1670Article in journal (Refereed)
    Abstract [en]

    In this article we provide a formal framework for reidentification in general. We define n-confusion as a concept for modeling the anonymity of a database table and we prove that n-confusion is a generalization of k-anonymity. After a short survey on the different available definitions of k-anonymity for graphs we provide a new definition for k-anonymous graph, which we consider to be the correct definition. We provide a description of the k-anonymous graphs, both for the regular and the non-regular case. We also introduce the more flexible concept of (k, l)-anonymous graph. Our definition of (k, l)-anonymous graph is meant to replace a previous definition of (k, l)-anonymous graph, which we here prove to have severe weaknesses. Finally, we provide a set of algorithms for k-anonymization of graphs.

  • 2.
    Torra, Vicenç
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. Hamilton Institute, Maynooth University, Ireland.
    Narukawa, Yasuo
    Department of Management Science, Tamagawa University, Japan.
    On network analysis using non-additive integrals: extending the game-theoretic network centrality2019In: Soft Computing - A Fusion of Foundations, Methodologies and Applications, ISSN 1432-7643, E-ISSN 1433-7479, Vol. 23, no 7, p. 2321-2329Article in journal (Refereed)
    Abstract [en]

    There are large amounts of information that can be represented in terms of graphs. This includes social networks and internet. We can represent people and their interactions by means of graphs. Similarly, we can represent web pages (and sites) as well as links between pages by means of graphs. In order to study the properties of graphs, several indices have been defined. They include degree centrality, betweenness, and closeness. In this paper, we propose the use of Choquet and Sugeno integrals with respect to non-additive measures for network analysis. This is a natural extension of the use of game theory for network analysis. Recall that monotonic games in game theory are non-additive measures. We discuss the expected force, a centrality measure, in the light of non-additive integral network analysis. We also show that some results by Godo et al. can be used to compute network indices when the information associated with a graph is qualitative.

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