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    Integral Privacy Compliant Statistics Computation

    Senavirathne, Navoda and Torra, Vicenç (2019) Integral Privacy Compliant Statistics Computation. In: Data Privacy Management, Cryptocurrencies and Blockchain Technology. Lecture Notes in Computer Science book series (LNCS) (11737). Springer, pp. 22-38. ISBN 9783030314996

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    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.

    Item Type: Book Section
    Additional Information: Cite as: Senavirathne N., Torra V. (2019) Integral Privacy Compliant Statistics Computation. In: Pérez-Solà C., Navarro-Arribas G., Biryukov A., Garcia-Alfaro J. (eds) Data Privacy Management, Cryptocurrencies and Blockchain Technology. DPM 2019, CBT 2019. Lecture Notes in Computer Science, vol 11737. Springer, Cham. . s This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
    Keywords: Privacy-preserving statistics; Privacy-preseving data analysis; Descriptive statistics;
    Academic Unit: Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 14382
    Identification Number:
    Depositing User: Vicenç Torra
    Date Deposited: 27 Apr 2021 14:17
    Publisher: Springer
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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