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Halas, R., Mesiar, R., Pocs, J. & Torra, V. (2019). A note on some algebraic properties of discrete Sugeno integrals. Fuzzy sets and systems (Print), 355, 110-120
Open this publication in new window or tab >>A note on some algebraic properties of discrete Sugeno integrals
2019 (English)In: Fuzzy sets and systems (Print), ISSN 0165-0114, E-ISSN 1872-6801, Vol. 355, p. 110-120Article in journal (Refereed) Published
Abstract [en]

Based on the link between Sugeno integrals and fuzzy measures, we discuss several algebraic properties of discrete Sugeno integrals. We recall that the composition of Sugeno integrals is again a Sugeno integral, and that each Sugeno integral can be obtained as a composition of binary Sugeno integrals. In particular, we discuss the associativity, dominance, commuting and bisymmetry of Sugeno integrals.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Associativity, L-measure, Sugeno integral
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-16471 (URN)10.1016/j.fss.2018.01.009 (DOI)000450287700009 ()2-s2.0-85041636724 (Scopus ID)
Available from: 2018-12-06 Created: 2018-12-06 Last updated: 2019-02-14Bibliographically approved
Torra, V., Karlsson, A., Steinhauer, H. J. & Berglund, S. (2019). Artificial Intelligence. In: Alan Said, Vicenç Torra (Ed.), Data Science in Practice: (pp. 9-26). Springer
Open this publication in new window or tab >>Artificial Intelligence
2019 (English)In: Data Science in Practice / [ed] Alan Said, Vicenç Torra, Springer, 2019, p. 9-26Chapter in book (Refereed)
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.

Place, publisher, year, edition, pages
Springer, 2019
Series
Studies in Big Data, ISSN 2197-6503, E-ISSN 2197-6511 ; 46
National Category
Computer and Information Sciences Philosophy Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); Consciousness and Cognitive Neuroscience
Identifiers
urn:nbn:se:his:diva-16784 (URN)10.1007/978-3-319-97556-6_2 (DOI)000464719500003 ()978-3-319-97556-6 (ISBN)978-3-319-97555-9 (ISBN)
Available from: 2019-04-17 Created: 2019-04-17 Last updated: 2019-09-30Bibliographically approved
Bae, J., Karlsson, A., Mellin, J., Ståhl, N. & Torra, V. (2019). Complex Data Analysis. In: Alan Said, Vicenç Torra (Ed.), Data science in Practice: (pp. 157-169). Springer
Open this publication in new window or tab >>Complex Data Analysis
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2019 (English)In: Data science in Practice / [ed] Alan Said, Vicenç Torra, Springer, 2019, p. 157-169Chapter in book (Refereed)
Abstract [en]

Data science applications often need to deal with data that does not fit into the standard entity-attribute-value model. In this chapter we discuss three of these other types of data. We discuss texts, images and graphs. The importance of social media is one of the reason for the interest on graphs as they are a way to represent social networks and, in general, any type of interaction between people. In this chapter we present examples of tools that can be used to extract information and, thus, analyze these three types of data. In particular, we discuss topic modeling using a hierarchical statistical model as a way to extract relevant topics from texts, image analysis using convolutional neural networks, and measures and visual methods to summarize information from graphs.

Place, publisher, year, edition, pages
Springer, 2019
Series
Studies in Big Data, ISSN 2197-6503, E-ISSN 2197-6511 ; 46
National Category
Computer and Information Sciences Computer Sciences Other Computer and Information Science
Research subject
Skövde Artificial Intelligence Lab (SAIL); Distributed Real-Time Systems
Identifiers
urn:nbn:se:his:diva-16811 (URN)10.1007/978-3-319-97556-6_9 (DOI)000464719500010 ()978-3-319-97556-6 (ISBN)978-3-319-97555-9 (ISBN)
Available from: 2019-04-24 Created: 2019-04-24 Last updated: 2019-09-30Bibliographically approved
Torra, V., Navarro-Arribas, G. & Stokes, K. (2019). Data privacy. In: Alan Said, Vicenç Torra (Ed.), Data science in Practice: (pp. 121-132). Springer
Open this publication in new window or tab >>Data privacy
2019 (English)In: Data science in Practice / [ed] Alan Said, Vicenç Torra, Springer, 2019, p. 121-132Chapter in book (Refereed)
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.

Place, publisher, year, edition, pages
Springer, 2019
Series
Studies in Big Data, ISSN 2197-6503, E-ISSN 2197-6511 ; 46
National Category
Computer and Information Sciences Other Computer and Information Science Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); Physics and Mathematics
Identifiers
urn:nbn:se:his:diva-16766 (URN)10.1007/978-3-319-97556-6_7 (DOI)000464719500008 ()978-3-319-97556-6 (ISBN)978-3-319-97555-9 (ISBN)
Available from: 2019-04-11 Created: 2019-04-11 Last updated: 2019-08-19Bibliographically approved
Said, A. & Torra, V. (2019). Data Science: An Introduction. In: Alan Said, Vicenç Torra (Ed.), Data Science in Practice: (pp. 1-6). Springer
Open this publication in new window or tab >>Data Science: An Introduction
2019 (English)In: Data Science in Practice / [ed] Alan Said, Vicenç Torra, Springer, 2019, p. 1-6Chapter in book (Refereed)
Abstract [en]

This chapter gives a general introduction to data science as a concept and to the topics covered in this book. First, we present a rough definition of data science, and point out how it relates to the areas of statistics, machine learning and big data technologies. Then, we review some of the most relevant tools that can be used in data science ranging from optimization to software. We also discuss the relevance of building models from data. The chapter ends with a detailed review of the structure of the book.

Place, publisher, year, edition, pages
Springer, 2019
Series
Studies in Big Data, ISSN 2197-6503, E-ISSN 2197-6511 ; 46
National Category
Computer and Information Sciences Other Computer and Information Science
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-16778 (URN)10.1007/978-3-319-97556-6_1 (DOI)000464719500002 ()978-3-319-97556-6 (ISBN)978-3-319-97555-9 (ISBN)
Available from: 2019-04-15 Created: 2019-04-15 Last updated: 2019-09-30Bibliographically approved
Torra, V. & Salas, J. (2019). Graph Perturbation as Noise Graph Addition: A New Perspective for Graph Anonymization. In: Data Privacy Management, Cryptocurrencies and Blockchain Technology: . Paper presented at ESORICS 2019 International Workshops, DPM 2019 and CBT 2019, Luxembourg, September 26–27, 2019 (pp. 121-137). Springer
Open this publication in new window or tab >>Graph Perturbation as Noise Graph Addition: A New Perspective for Graph Anonymization
2019 (English)In: Data Privacy Management, Cryptocurrencies and Blockchain Technology, 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
Springer, 2019
Series
Lecture Notes in Computer Science, ISSN 03029743, E-ISSN 1611-3349
Keywords
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
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-18009 (URN)10.1007/978-3-030-31500-9_8 (DOI)2-s2.0-85075616311 (Scopus ID)9783030314996 (ISBN)978-3-030-31500-9 (ISBN)
Conference
ESORICS 2019 International Workshops, DPM 2019 and CBT 2019, Luxembourg, September 26–27, 2019
Available from: 2019-12-12 Created: 2019-12-12 Last updated: 2019-12-12
Senavirathne, N. & Torra, V. (2019). Integral Privacy Compliant Statistics Computation. In: Cristina Pérez-Solà, Guillermo Navarro-Arribas, Alex Biryukov, Joaquin Garcia-Alfaro (Ed.), Data Privacy Management, Cryptocurrencies and Blockchain Technology: ESORICS 2019 International Workshops, DPM 2019 and CBT 2019, Luxembourg, September 26–27, 2019, Proceedings. Paper presented at ESORICS 2019 International Workshops, DPM 2019 and CBT 2019, Luxembourg, September 26–27, 2019 (pp. 22-38). Cham: Springer, 11737
Open this publication in new window or tab >>Integral Privacy Compliant Statistics Computation
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, Vol. 11737, p. 22-38Conference paper, Published paper (Refereed)
Abstract [en]

Data analysis is expected to provide accurate descriptions of the data. However, this is in opposition to privacy requirements when working with sensitive data. In this case, there is a need to ensure that no disclosure of sensitive information takes place by releasing the data analysis results. Therefore, privacy-preserving data analysis has become significant. Enforcing strict privacy guarantees can significantly distort data or the results of the data analysis, thus limiting their analytical utility (i.e., differential privacy). In an attempt to address this issue, in this paper we discuss how “integral privacy”; a re-sampling based privacy model; can be used to compute descriptive statistics of a given dataset with high utility. In integral privacy, privacy is achieved through the notion of stability, which leads to release of the least susceptible data analysis result towards the changes in the input dataset. Here, stability is explained by the relative frequency of different generators (re-samples of data) that lead to the same data analysis results. In this work, we compare the results of integrally private statistics with respect to different theoretical data distributions and real world data with differing parameters. Moreover, the results are compared with statistics obtained through differential privacy. Finally, through empirical analysis, it is shown that the integral privacy based approach has high utility and robustness compared to differential privacy. Due to the computational complexity of the method we propose that integral privacy to be more suitable towards small datasets where differential privacy performs poorly. However, adopting an efficient re-sampling mechanism can further improve the computational efficiency in terms of integral privacy. © 2019, The Author(s).

Place, publisher, year, edition, pages
Cham: Springer, 2019
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11737
Keywords
Descriptive statistics, Privacy-preserving statistics, Privacy-preseving data analysis, Blockchain, Computational efficiency, Computer privacy, Electronic money, Information analysis, Sampling, Statistics, Data distribution, Differential privacies, Empirical analysis, Privacy preserving, Privacy requirements, Relative frequencies, Sensitive informations, Data privacy
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-18008 (URN)10.1007/978-3-030-31500-9_2 (DOI)2-s2.0-85075604651 (Scopus ID)978-3-030-31499-6 (ISBN)978-3-030-31500-9 (ISBN)
Conference
ESORICS 2019 International Workshops, DPM 2019 and CBT 2019, Luxembourg, September 26–27, 2019
Available from: 2019-12-12 Created: 2019-12-12 Last updated: 2019-12-13Bibliographically approved
Senavirathne, N. & Torra, V. (2019). Integrally private model selection for decision trees. Computers & security (Print), 83, 167-181
Open this publication in new window or tab >>Integrally private model selection for decision trees
2019 (English)In: Computers & security (Print), ISSN 0167-4048, E-ISSN 1872-6208, Vol. 83, p. 167-181Article in journal (Refereed) Published
Abstract [en]

Privacy attacks targeting machine learning models are evolving. One of the primary goals of such attacks is to infer information about the training data used to construct the models. “Integral Privacy” focuses on machine learning and statistical models which explain how we can utilize intruder's uncertainty to provide a privacy guarantee against model comparison attacks. Through experimental results, we show how the distribution of models can be used to achieve integral privacy. Here, we observe two categories of machine learning models based on their frequency of occurrence in the model space. Then we explain the privacy implications of selecting each of them based on a new attack model and empirical results. Also, we provide recommendations for private model selection based on the accuracy and stability of the models along with the diversity of training data that can be used to generate the models. 

Place, publisher, year, edition, pages
Elsevier Ltd, 2019
Keywords
Data privacy, Integral privacy, Machine learning model space, Privacy models, Privacy preserving machine learning, Decision trees, Attack model, Machine learning models, Model comparison, Model Selection, Privacy Attacks, Privacy preserving, Training data, Machine learning
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-16682 (URN)10.1016/j.cose.2019.01.006 (DOI)000465367100013 ()2-s2.0-85062062700 (Scopus ID)
Available from: 2019-03-08 Created: 2019-03-08 Last updated: 2019-07-10Bibliographically approved
Torra, V. & Senavirathne, N. (2019). Maximal c consensus meets. Information Fusion, 51, 58-66
Open this publication in new window or tab >>Maximal c consensus meets
2019 (English)In: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 51, p. 58-66Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
NETHERLANDS: Elsevier, 2019
Keywords
clustering, consensus clustering, heuristic algorithms, Maximal c consensus meets, Cluster analysis, Clustering algorithms, Lattice theory, Set theory, Reference set
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-16463 (URN)10.1016/j.inffus.2018.09.011 (DOI)000469155600006 ()2-s2.0-85056612105 (Scopus ID)
Available from: 2019-01-30 Created: 2019-01-30 Last updated: 2019-07-10Bibliographically approved
Torra, V. & Narukawa, Y. (2019). On network analysis using non-additive integrals: extending the game-theoretic network centrality. Soft Computing - A Fusion of Foundations, Methodologies and Applications, 23(7), 2321-2329
Open this publication in new window or tab >>On network analysis using non-additive integrals: extending the game-theoretic network centrality
2019 (English)In: 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) Published
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.

Place, publisher, year, edition, pages
Springer, 2019
Keywords
Non-additive measures and integrals, Graphs, Aggregation, Network analysis
National Category
Computer Sciences
Research subject
Infection Biology
Identifiers
urn:nbn:se:his:diva-16741 (URN)10.1007/s00500-018-03710-9 (DOI)000461580400016 ()2-s2.0-85059054946 (Scopus ID)
Available from: 2019-04-04 Created: 2019-04-04 Last updated: 2019-09-30Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-0368-8037

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