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Nanni, M., Andrienko, G., Barabási, A.-L., Boldrini, C., Bonchi, F., Cattuto, C., . . . Vespignani, A. (2020). Give more data, awareness and control to individual citizens, and they will help COVID-19 containment. Transactions on Data Privacy, 13(1), 61-66
Open this publication in new window or tab >>Give more data, awareness and control to individual citizens, and they will help COVID-19 containment
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2020 (English)In: Transactions on Data Privacy, ISSN 1888-5063, E-ISSN 2013-1631, Vol. 13, no 1, p. 61-66Article in journal (Refereed) Published
Abstract [en]

The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the "phase 2" of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens' privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens' "personal data stores", to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allowthe user to share spatio-temporal aggregates - if and when they want and for specific aims - with health authorities, for instance. Second, we favour a longerterm pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.

Place, publisher, year, edition, pages
Institut d'Investigació en Intel·ligència Artificial, 2020
Keywords
COVID-19, Personal Data Store, mobility data analysis, contact tracing
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-18433 (URN)000528181000003 ()2-s2.0-85084115690 (Scopus ID)
Available from: 2020-05-07 Created: 2020-05-07 Last updated: 2020-05-12Bibliographically approved
Salas, J., Megías, D., Torra, V., Toger, M., Dahne, J. & Sainudiin, R. (2020). Swapping trajectories with a sufficient sanitizer. Pattern Recognition Letters, 131, 474-480
Open this publication in new window or tab >>Swapping trajectories with a sufficient sanitizer
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2020 (English)In: Pattern Recognition Letters, ISSN 0167-8655, E-ISSN 1872-7344, Vol. 131, p. 474-480Article in journal (Refereed) Published
Abstract [en]

Real-time mobility data is useful for several applications such as planning transports in metropolitan areas or localizing services in towns. However, if such data is collected without any privacy protection it may reveal sensible locations and pose safety risks to an individual associated to it. Thus, mobility data must be anonymized preferably at the time of collection. In this paper, we consider the SwapMob algorithm that mitigates privacy risks by swapping partial trajectories. We formalize the concept of sufficient sanitizer and show that the SwapMob algorithm is a sufficient sanitizer for various statistical decision problems. That is, it preserves the aggregate information of the spatial database in the form of sufficient statistics and also provides privacy to the individuals. This may be used for personalized assistants taking advantage of users’ locations, so they can ensure user privacy while providing accurate response to the user requirements. We measure the privacy provided by SwapMob as the Adversary Information Gain, which measures the capability of an adversary to leverage his knowledge of exact data points to infer a larger segment of the sanitized trajectory. We test the utility of the data obtained after applying SwapMob sanitization in terms of Origin-Destination matrices, a fundamental tool in transportation modelling.

Place, publisher, year, edition, pages
Elsevier, 2020
Keywords
Intelligent transportation systems, Origin-Destination matrices, Privacy preserving mobility data mining, Real-time mobility data anonymization, Sufficient sanitizer, Trajectory anonymization, Data mining, Intelligent systems, Knowledge management, Matrix algebra, Real time systems, Trajectories, Anonymization, Mobility datum, Origin destination matrices, Data privacy
National Category
Computer Sciences Transport Systems and Logistics
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-18264 (URN)10.1016/j.patrec.2020.02.011 (DOI)000521971700064 ()2-s2.0-85079419408 (Scopus ID)
Available from: 2020-02-28 Created: 2020-02-28 Last updated: 2020-04-22Bibliographically approved
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: 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. 121-137). Cham: 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: 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, 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
Cham: Springer, 2019
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11737
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
National Category
Computer Sciences
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)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
Note

Also part of the Security and Cryptology book sub series (LNSC, volume 11737)

Available from: 2019-12-12 Created: 2019-12-12 Last updated: 2020-01-29Bibliographically approved
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: 2020-01-29Bibliographically 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
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-0368-8037

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