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  • 51.
    Torra, Vicenç
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Steinhauer, H. Joe
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Berglund, Stefan
    Högskolan i Skövde, Institutionen för biovetenskap. Högskolan i Skövde, Forskningscentrum för Systembiologi.
    Artificial Intelligence2019Inngår i: Data Science in Practice / [ed] Alan Said, Vicenç Torra, Springer, 2019, s. 9-26Kapittel i bok, del av antologi (Fagfellevurdert)
    Abstract [en]

    This chapter gives a brief introduction to what artificial intelligence is. We begin discussing some of the alternative definitions for artificial intelligence and introduce the four major areas of the field. Then, in subsequent sections we present these areas. They are problem solving and search, knowledge representation and knowledge-based systems, machine learning, and distributed artificial intelligence. The chapter follows with a discussion on some ethical dilemma we find in relation to artificial intelligence. A summary closes this chapter.

  • 52.
    Torra, Vicenç
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Narukawa, Yasuo
    Distances on non-additive measures using the numerical Choquet integrale2015Inngår i: The 12th International Conference on Modeling Decisions for Artificial Intelligence: CD-ROM Proceedings, MDAI - HiS , 2015Konferansepaper (Fagfellevurdert)
  • 53.
    Torra, Vicenç
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Narukawa, YasuoToho Gakuen, Tokyo, Japan.
    Modeling Decisions for Artificial Intelligence: 12th International Conference, MDAI 2015, Skövde, Sweden, September 21-23, 2015, Proceedings2015Konferanseproceedings (Fagfellevurdert)
  • 54.
    Torra, Vicenç
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Narukawa, Yasuo
    Toho Gakuen, Naka, Kunitachi, Tokyo, Japan.
    Numerical integration for the Choquet integral2016Inngår i: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 31, s. 137-145Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Choquet integrals with respect to non-additive (or fuzzy measures) have been used in a large number of applications because they permit us to integrate information from different sources when there are interactions. Successful applications use a discrete reference set. In the case of measures on a continuous reference set, as e.g. the real line, few results have been obtained that permit us to have an analytical expression of the integral. However, in most of the cases there is no such analytical expression. In this paper we describe how to perform the numerical integration of a Choquet integral with respect to a non-additive measure.

  • 55.
    Torra, Vicenç
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. 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 centrality2019Inngår i: Soft Computing - A Fusion of Foundations, Methodologies and Applications, ISSN 1432-7643, E-ISSN 1433-7479, Vol. 23, nr 7, s. 2321-2329Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 56.
    Torra, Vicenç
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Narukawa, Yasuo
    Toho Gakuen, Naka, Kunitachi, Tokyo, Japan / Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Nagatuta, Midori-ku, Yokohama, Japan.
    Dahlbom, Anders
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    On this book: Clustering, multisets, rough sets and fuzzy sets2017Inngår i: Fuzzy sets, rough sets, multisets and clustering / [ed] Vicenç Torra, Anders Dahlbom & Yasuo Narukawa, Springer, 2017, s. 1-5Kapittel i bok, del av antologi (Annet vitenskapelig)
    Abstract [en]

    This chapter gives an overview of the content of this book, and links them with the work of Prof. Sadaaki Miyamoto, to whom this book is dedicated.

  • 57.
    Torra, Vicenç
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Narukawa, YasuoDahlbom, AndersHögskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    The 12th International Conference on Modeling Decisions for Artificial Intelligence: CD-ROM Proceedings2015Konferanseproceedings (Fagfellevurdert)
  • 58.
    Torra, Vicenç
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Narukawa, YasuoToho Gakuen, Kunitachi, Tokyo, Japan.Navarro-Arribas, GuillermoUniversitat Autònoma de Barcelona, Bellaterra, Spain.Yañez, CristinaUniversitat d’ Andorra, Sant Julià de Lòria, Andorra.
    Modeling Decisions for Artificial Intelligence: 13th International Conference, MDAI 2016Sant Juliàde Lòria, Andorra, September 19–21, 2016: Proceedings2016Konferanseproceedings (Fagfellevurdert)
  • 59.
    Torra, Vicenç
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. IIIA-CSIC, Campus UAB s/n, Bellaterra, Catalonia, Spain.
    Narukawa, Yasuo
    Toho Gakuen, Kunitachi, Tokyo, Japan.
    Sugeno, Michio
    ECSC, c/ Gonzalo Gutiérrez Quirós s/n, Mieres, Spain.
    On the f-divergence for non-additive measures2016Inngår i: Fuzzy sets and systems (Print), ISSN 0165-0114, E-ISSN 1872-6801, Vol. 292, s. 364-379Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The f -divergence evaluates the dissimilarity between two probability distributions defined in terms of the Radon–Nikodym derivative of these two probabilities. The f -divergence generalizes the Hellinger distance and the Kullback–Leibler divergence among other divergence functions. In this paper we define an analogous function for non-additive measures. We discuss them for distorted Lebesgue measures and give examples. Examples focus on the Hellinger distance.

  • 60.
    Torra, Vicenç
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Narukawa, Yasuo
    Toho Gakuen, Kunitachi, Japan.
    Yager, Ronald R.
    Iona College, New Rochelle, NY, USA.
    On a Relationship Between Fuzzy Measures and AIFS2016Inngår i: International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, ISSN 0218-4885, Vol. 24, nr 6, s. 847-858Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The literature discusses several extensions of fuzzy sets. AIFS, IVFS, HFS, type-2 fuzzy sets are some of them. Interval valued fuzzy sets is one of the extensions where the membership is not a single value but an interval. Atanassov Intuitionistic fuzzy sets, for short AIFS, are defined in terms of two values for each element: membership and non-membership. In this paper we discuss AIFS and their relationship with fuzzy measures. The discussion permits us to define counter AIFS (cIFS) and discretionary AIFS (dIFS). They are extensions of fuzzy sets that are based on fuzzy measures.

  • 61.
    Torra, Vicenç
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Navarro-Arribas, Guillermo
    Department of Information and Communication Engineering, Universitat Autònoma de Barcelona, Catalonia, Spain.
    Big Data Privacy and Anonymization2016Inngår i: Privacy and Identity Management. Facing up to Next Steps: 11th IFIP WG 9.2, 9.5, 9.6/11.7, 11.4, 11.6/SIG 9.2.2 International Summer School, Karlstad, Sweden, August 21-26, 2016, Revised Selected Papers / [ed] Anja Lehmann, Diane Whitehouse, Simone Fischer-Hübner, Lothar Fritsch, Charles Raab, Springer, 2016, s. 15-26Kapittel i bok, del av antologi (Fagfellevurdert)
    Abstract [en]

    Data privacy has been studied in the area of statistics (statistical disclosure control) and computer science (privacy preserving data mining and privacy enhancing technologies) for at least 40 years. In this period models, measures, methods, and technologies have been developed to effectively protect the disclosure of sensitive information.

    The coming of big data, with large volumes of data, dynamic and streaming data, poses new challenges to the field. In this paper we will review some of these challenges and propose some lines of research in the field.

  • 62.
    Torra, Vicenç
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Navarro-Arribas, Guillermo
    Department of Information and Communication Engineering, Universitat Autònoma de Barcelona, Catalonia Spain.
    Integral privacy2016Inngår i: Cryptology and Network Security: 15th International Conference, CANS 2016 Milan, Italy, November 14–16, 2016 Proceedings / [ed] Sara Foresti and Giuseppe Persiano, Springer, 2016, Vol. 10052, s. 661-669Konferansepaper (Fagfellevurdert)
    Abstract [en]

    When considering data provenance some problems arise from the need to safely handle provenance related functionality. If some modifications have to be performed in a data set due to provenance related requirements, e.g. remove data from a given user or source, this will affect not only the data itself but also all related models and aggregated information obtained from the data. This is specially aggravated when the data are protected using a privacy method (e.g. masking method), since modification in the data and the model can leak information originally protected by the privacy method. To be able to evaluate privacy related problems in data provenance we introduce the notion of integral privacy as compared to the well known definition of differential privacy

  • 63.
    Torra, Vicenç
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Navarro-Arribas, Guillermo
    Department of Information and Communications Engineering, CYBERCAT-Center for Cybersecurity Research of Catalonia, Universitat Aut`onoma de Barcelona, Spain.
    Probabilistic Metric Spaces for Privacy by Design Machine Learning Algorithms: Modeling Database Changes2018Inngår i: Data Privacy Management, Cryptocurrencies and Blockchain Technology: ESORICS 2018 International Workshops, DPM 2018 and CBT 2018, Barcelona, Spain, September 6-7, 2018, Proceedings / [ed] Joaquin Garcia-Alfaro, Jordi Herrera-Joancomartí, Giovanni Livraga, Ruben Rios, Cham: Springer, 2018, Vol. 11025, s. 422-430Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Machine learning, data mining and statistics are used to analyze the data and to build models from them. Data privacy for big data needs to find a compromise between data analysis and disclosure risk. Privacy by design machine learning algorithms need to take into account the space of models and the relationship between the data that generates the models and the models themselves. In this paper we propose the use of probabilistic metric spaces for comparing these models.

  • 64.
    Torra, Vicenç
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Navarro-Arribas, Guillermo
    Department of Information and Communication Engineering, Universitat Autònoma de Barcelona, Barcelona, Spain.
    Sanchez-Charles, David
    CA Technologies, Barcelona, Spain.
    Muntes-Mulero, Victor
    CA Technologies, Barcelona, Spain.
    Provenance and Privacy2017Inngår i: Modeling Decisions for Artificial Intelligence: 14th International Conference, MDAI 2017 Kitakyushu, Japan, October 18–20, 2017 Proceedings / [ed] Torra V., Narukawa Y., Honda A., Inoue S., Springer, 2017, s. 3-11Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper presents an overview of current needs on data provenance and data privacy, and discusses state-of-the-art results in this area. The paper highlights the difficulties that we need to face and finishes with some lines that require further work.

  • 65.
    Torra, Vicenç
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Navarro-Arribas, Guillermo
    Universitat Autònoma de Barcelona, Barcelona, Spain.
    Stokes, Klara
    Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system.
    An Overview of the Use of Clustering for Data Privacy2016Inngår i: Unsupervised Learning Algorithms / [ed] M. Emre Celebi, Kemal Aydin, Springer, 2016, s. 237-251Kapittel i bok, del av antologi (Fagfellevurdert)
    Abstract [en]

    In this chapter we review some of our results related to the use of clustering in the area of data privacy. The paper gives a brief overview of data privacy and, more specifically, on data driven methods for data privacy and discusses where clustering can be applied in this setting. We discuss the role of clustering in the definition of masking methods, and on the calculation of information loss and data utility.

  • 66.
    Torra, Vicenç
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Navarro-Arribas, Guillermo
    Universitat Autònoma de Barcelona, Campus UAB, Bellaterra, Spain.
    Stokes, Klara
    Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system.
    Data privacy2019Inngår i: Data science in Practice / [ed] Alan Said, Vicenç Torra, Springer, 2019, s. 121-132Kapittel i bok, del av antologi (Fagfellevurdert)
    Abstract [en]

    In this chapter we present an overview of the topic data privacy. We review privacy models and measures of disclosure risk. These models and measures provide computational definitions of what privacy means, and of how to evaluate the privacy level of a data set. Then, we give a summary of data protection mechanisms. We provide a classification of these methods according to three dimensions: whose privacy is being sought, the computations to be done, and the number of data sources. Finally, we describe masking methods. Such methods are the data protection mechanisms used for databases when the data use is undefined and the protected database is required to be useful for several data uses. We also provide a definition of information loss (or data utility) for this type of data protection mechanism. The chapter finishes with a summary.

  • 67.
    Torra, Vicenç
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. Hamilton Institute, Maynooth University, Ireland.
    Salas, Julián
    CYBERCAT-Center for Cybersecurity Research of Catalonia, Internet Interdisciplinary Institute (IN3), Universitat Oberta de Catalunya, Barcelona, Spain.
    Graph Perturbation as Noise Graph Addition: A New Perspective for Graph Anonymization2019Inngår i: 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, s. 121-137Konferansepaper (Fagfellevurdert)
    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. 

  • 68.
    Torra, Vicenç
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Senavirathne, Navoda
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Maximal c consensus meets2019Inngår i: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 51, s. 58-66Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Given a set S of subsets of a reference set X, we define the problem of finding c subsets of X that maximize the size of the intersection among the included subsets. Maximizing the size of the intersection means that they are subsets of the sets in S and they are as large as possible. We can understand the result of this problem as c consensus sets of S, or c consensus representatives of S. From the perspective of lattice theory, each representative will be a meet of some sets in S. In this paper we define formally this problem, and present heuristic algorithms to solve it. We also discuss the relationship with other established problems in the literature.

  • 69.
    Torra, Vicenç
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Shafie, Termeh
    University of Konstanz, Konstanz, Germany.
    Salas, Julián
    Artificial Intelligence Research Institute(IIIA)–Spanish National Research Council (CSIC), Bellaterra 08193, Catalonia, Spain.
    Data Protection for Online Social Networks and P-Stability for Graphs2016Inngår i: IEEE Transactions on Emerging Topics in Computing, ISSN 2168-6750, Vol. 4, nr 3, s. 374-381Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Graphs can be used as a model for online social networks. In this framework, vertices represent individuals and edges relationships between individuals. In recent years, different approaches have been considered to offer data privacy to online social networks and for developing graph protection. Perturbative approaches are formally defined in terms of perturbation and modification of graphs. In this paper, we discuss the concept of P-stability on graphs and its relation to data privacy. The concept of P-stability is rooted in the number of graphs given a fixed degree sequence. In this paper, we show that for any graph there exists a class of P-stable graphs. This result implies that there is a fully polynomial randomized approximation for graph masking for the graphs in the class. In order to further refine the classification of a given graph, we introduce the concept of natural class of a graph. It is based on a class of scale-free networks.

  • 70.
    Torra, Vicenç
    et al.
    Institut d'Investigacío en Intellig̀encia Artificial, Consejo Superior de Investigaciones Cient́ficas, Universitat Aut̀onoma de Barcelona, Bellaterra, Catalonia, Spain.
    Stokes, Klara
    Departments of Computer Science and Mathematics, Universitat Rovira i Virgili, Tarragona, Spain / UNESCO Chair in Data Privacy, Tarragona, Catalonia, Spain.
    Narukawa, Yasuo
    Toho Gakuen, Tokyo, Japan / Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama, Japan.
    An extension of fuzzy measures to multisets and its relation to distorted probabilities2012Inngår i: IEEE transactions on fuzzy systems, ISSN 1063-6706, E-ISSN 1941-0034, Vol. 20, nr 6, s. 1032-1045Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Fuzzy measures are monotonic set functions on a reference set; they generalize probabilities replacing the additivity condition by monotonicity. The typical application of these measures is with fuzzy integrals. Fuzzy integrals integrate a function with respect to a fuzzy measure, and they can be used to aggregate information from a set of sources (opinions from experts or criteria in a multicriteria decision-making problem). In this context, background knowledge on the sources is represented by means of the fuzzy measures. For example, interactions between criteria are represented by means of nonadditive measures. In this paper, we introduce fuzzy measures on multisets. We propose a general definition, and we then introduce a family of fuzzy measures for multisets which we show to be equivalent to distorted probabilities when the multisets are restricted to proper sets

12 51 - 70 of 70
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