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On Privacy of Multidimensional Data Against Aggregate Knowledge Attacks

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Privacy in Statistical Databases (PSD 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13463))

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Abstract

In this paper, we explore the privacy problem of individuals in publishing data cubes using SUM queries, where a malicious user is expected to have an aggregate knowledge (e.g., average information) over the data ranges. We propose an efficient solution that maximizes the utility of SUM queries while mitigating inference attacks from aggregate knowledge. Our solution combines cube compression (i.e., suppression of data cells) and data perturbation. First, we give a formal statement for the privacy of aggregate knowledge based on data suppression. Next, we develop a Linear Programming (LP) model to determine the number of data cells to be removed and a heuristic method to effectively suppress data cells. To overcome the limitation of data suppression, we complement it with suitable data perturbation. Through empirical evaluation on benchmark data cubes, we show that our solution gives best performance in terms of utility and privacy.

This work is funded by DigiTrust (http://lue.univ-lorraine.fr/fr/article/digitrust/).

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Notes

  1. 1.

    https://github.com/AlaEddineLaouir/PUV-CUBE.

  2. 2.

    https://docs.microsoft.com/fr-fr/analysis-services/?view=asallproducts-allversions.

  3. 3.

    https://www.tpc.org/tpcds/.

  4. 4.

    https://github.com/Microsoft/sql-server-samples/releases/download/adventureworks/AdventureWorks2012.bak.

  5. 5.

    https://developers.google.com/optimization.

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Authors and Affiliations

  1. Lorraine University, Cnrs, Inria, 54506, Vandœuvre-lès-Nancy, France

    Ala Eddine Laouir & Abdessamad Imine

Authors
  1. Ala Eddine Laouir
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  2. Abdessamad Imine
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Correspondence to Ala Eddine Laouir .

Editor information

Editors and Affiliations

  1. Universitat Rovira i Virgili, Tarragona, Catalonia, Spain

    Josep Domingo-Ferrer

  2. Télécom SudParis, Palaiseau, France

    Maryline Laurent

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Laouir, A.E., Imine, A. (2022). On Privacy of Multidimensional Data Against Aggregate Knowledge Attacks. In: Domingo-Ferrer, J., Laurent, M. (eds) Privacy in Statistical Databases. PSD 2022. Lecture Notes in Computer Science, vol 13463. Springer, Cham. https://doi.org/10.1007/978-3-031-13945-1_7

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  • DOI: https://doi.org/10.1007/978-3-031-13945-1_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13944-4

  • Online ISBN: 978-3-031-13945-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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