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  • 1.
    Abril, Daniel
    et al.
    IIIA, Institut d'Investigació en Intelligència Artificial – CSIC, Consejo Superior de Investigaciones Científicas, Bellaterra, Spain / UAB, Universitat Autónoma de Barcelona, Bellaterra, Spain.
    Navarro-Arribas, Guillermo
    DEIC, Dep. Enginyeria de la Informació i de les Comunicacions, UAB, Universitat Autònoma de Barcelona, Bellaterra, Spain.
    Torra, Vicenç
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. IIIA, Institut d'Investigació en Intelligència Artificial – CSIC, Consejo Superior de Investigaciones Científicas, Bellaterra, Spain.
    Spherical Microaggregation: Anonymizing Sparse Vector Spaces2015In: Computers & security (Print), ISSN 0167-4048, E-ISSN 1872-6208, Vol. 49, p. 28-44Article in journal (Refereed)
    Abstract [en]

    Unstructured texts are a very popular data type and still widely unexplored in the privacy preserving data mining field. We consider the problem of providing public information about a set of confidential documents. To that end we have developed a method to protect a Vector Space Model (VSM), to make it public even if the documents it represents are private. This method is inspired by microaggregation, a popular protection method from statistical disclosure control, and adapted to work with sparse and high dimensional data sets.

  • 2.
    Senavirathne, Navoda
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Torra, Vicenç
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. Maynooth University Hamilton Institute, Kildare, Ireland.
    Integrally private model selection for decision trees2019In: Computers & security (Print), ISSN 0167-4048, E-ISSN 1872-6208, Vol. 83, p. 167-181Article in journal (Refereed)
    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. 

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