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  • 1.
    Senavirathne, Navoda
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Addressing the challenges of privacy preserving machine learning in the context of data anonymization2019Report (Other academic)
    Abstract [en]

    Machine learning (ML) models trained on sensitive data pose a distinct threat to privacy with the emergence of numerous threat models exploiting their privacy vulnerabilities.Therefore, privacy preserving machine learning (PPML) has gained an increased attentionover the past couple of years. Existing PPML techniques introduced in the literatureare mainly based on differential privacy or cryptography based techniques. Respectivelythey are criticized for the poor predictive accuracy of the derived ML models and for theextensive computational cost. Moreover, they operate under the assumption that originaldata are always available for training the ML models. However, there exist scenarioswhere anonymized data are available instead of the original data. Anonymization ofsensitive data is required before publishing them in order to preserve the privacy of theunderlying data subjects. Nevertheless, there are valid organizational and legal requirementsfor data publishing. In this case, it is important to understand the impact of dataanonymization on ML in general and how this can be used as a stepping stone towardsPPML.The proposed research is aimed at understanding the opportunities and challenges forPPML in the context of data anonymization, and to address them effectively by developinga unified solution to serve the objectives of both data anonymization and PPML.

  • 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.
    Approximating Robust Linear Regression With An Integral Privacy Guarantee2018In: 2018 16th Annual Conference on Privacy, Security and Trust (PST) / [ed] Kieran McLaughlin, Ali Ghorbani, Sakir Sezer, Rongxing Lu, Liqun Chen, Robert H. Deng, Paul Miller, Stephen Marsh, Jason Nurse, IEEE, 2018, p. 85-94Conference paper (Refereed)
    Abstract [en]

    Most of the privacy-preserving techniques suffer from an inevitable utility loss due to different perturbations carried out on the input data or the models in order to gain privacy. When it comes to machine learning (ML) based prediction models, accuracy is the key criterion for model selection. Thus, an accuracy loss due to privacy implementations is undesirable. The motivation of this work, is to implement the privacy model "integral privacy" and to evaluate its eligibility as a technique for machine learning model selection while preserving model utility. In this paper, a linear regression approximation method is implemented based on integral privacy which ensures high accuracy and robustness while maintaining a degree of privacy for ML models. The proposed method uses a re-sampling based estimator to construct linear regression model which is coupled with a rounding based data discretization method to support integral privacy principles. The implementation is evaluated in comparison with differential privacy in terms of privacy, accuracy and robustness of the output ML models. In comparison, integral privacy based solution provides a better solution with respect to the above criteria.

  • 3.
    Senavirathne, Navoda
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. Hamilton Institute, Maynooth University, Maynooth, Ireland.
    Torra, Vicenç
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. Hamilton Institute, Maynooth University, Maynooth, Ireland.
    Integral Privacy Compliant Statistics Computation2019In: 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 (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).

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  • 4.
    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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  • 5.
    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. Hamilton Institute, Maynooth University, Maynooth, Ireland.
    Rounding based continuous data discretization for statistical disclosure control2019In: Journal of Ambient Intelligence and Humanized Computing, ISSN 1868-5137, E-ISSN 1868-5145, p. 1-19Article in journal (Refereed)
    Abstract [en]

    “Rounding” can be understood as a way to coarsen continuous data. That is, low level and infrequent values are replaced by high-level and more frequent representative values. This concept is explored as a method for data privacy with techniques like rounding, microaggregation, and generalisation. This concept is explored as a method for data privacy in statistical disclosure control literature with perturbative techniques like rounding, microaggregation and non-perturbative methods like generalisation. Even though “rounding” is well known as a numerical data protection method, it has not been studied in depth or evaluated empirically to the best of our knowledge. This work is motivated by three objectives, (1) to study the alternative methods of obtaining the rounding values to represent a given continuous variable, (2) to empirically evaluate rounding as a data protection technique based on information loss (IL) and disclosure risk (DR), and (3) to analyse the impact of data rounding on machine learning based models. Here, in order to obtain the rounding values we consider discretization methods introduced in the unsupervised machine learning literature along with microaggregation and re-sampling based approaches. The results indicate that microaggregation based techniques are preferred over unsupervised discretization methods due to their fair trade-off between IL and DR. 

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  • 6.
    Torra, Vicenç
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Senavirathne, Navoda
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Maximal c consensus meets2019In: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 51, p. 58-66Article in journal (Refereed)
    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.

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