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  • 1.
    Abdelraheem, Mohamed Ahmed
    et al.
    SICS Swedish ICT AB, SWE.
    Gehrmann, Christian
    SICS Swedish ICT AB, SWE.
    Lindström, Malin
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Nordahl, Christian
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Executing Boolean queries on an encrypted Bitmap index2016In: CCSW 2016 - Proceedings of the 2016 ACM Cloud Computing Security Workshop, co-located with CCS 2016, Association for Computing Machinery (ACM), 2016, p. 11-22Conference paper (Refereed)
    Abstract [en]

    We propose a simple and efficient searchable symmetric encryption scheme based on a Bitmap index that evaluates Boolean queries. Our scheme provides a practical solution in settings where communications and computations are very constrained as it offers a suitable trade-off between privacy and performance.

  • 2.
    Nordahl, Christian
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Boeva, Veselka
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Grahn, Håkan
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Netz Persson, Marie
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Profiling of household residents’ electricity consumption behavior using clustering analysis2019In: Lect. Notes Comput. Sci., Springer Verlag , 2019, p. 779-786Conference paper (Refereed)
    Abstract [en]

    In this study we apply clustering techniques for analyzing and understanding households’ electricity consumption data. The knowledge extracted by this analysis is used to create a model of normal electricity consumption behavior for each particular household. Initially, the household’s electricity consumption data are partitioned into a number of clusters with similar daily electricity consumption profiles. The centroids of the generated clusters can be considered as representative signatures of a household’s electricity consumption behavior. The proposed approach is evaluated by conducting a number of experiments on electricity consumption data of ten selected households. The obtained results show that the proposed approach is suitable for data organizing and understanding, and can be applied for modeling electricity consumption behavior on a household level. © Springer Nature Switzerland AG 2019.

  • 3.
    Nordahl, Christian
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Grahn, Håkan
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Persson, Marie
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Boeva, Veselka
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Organizing, Visualizing and Understanding Households Electricity Consumption Data through Clustering Analysis.2018In: Organizing, Visualizing and Understanding Households Electricity Consumption Data through Clustering Analysis, https://sites.google.com/view/arial2018/accepted-papersprogram , 2018Conference paper (Refereed)
    Abstract [en]

    We propose a cluster analysis approach for organizing, visualizing and understanding households’ electricity consumption data. We initially partition the consumption data into a number of clusters with similar daily electricity consumption profiles. The centroids of each cluster can be seen as representative signatures of a household’s electricity consumption behaviors. We evaluate the proposed approach by conducting a number of experiments on electricity consumption data of ten selected households. Our results show that the approach is suitable for data analysis, understanding and creating electricity consumption behavior models.

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