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Breaching Privacy Using Data Mining: Removing Noise from Perturbed Data

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Computational Intelligence for Privacy and Security

Part of the book series: Studies in Computational Intelligence ((SCI,volume 394))

Abstract

Data perturbation is a sanitization method that helps restrict the disclosure of sensitive information from published data. We present an attack on the privacy of the published data that has been sanitized using data perturbation. The attack employs data mining and fusion to remove some noise from the perturbed sensitive values. Our attack is practical – it can be launched by non-expert adversaries having no background knowledge about the perturbed data and no data mining expertise. Moreover, our attack model also allows to consider informed and expert adversaries having background knowledge and/or expertise in data mining and fusion. Extensive experiments were performed on four databases derived from UCI’s Adult and IPUMS census-based data sets sanitized with noise addition that satisfies ε-differential privacy. The experimental results confirm that our attack presents a significant privacy risk to published perturbed data because the majority of the noise can be effectively removed. The results show that a naive adversary is able to remove around 90% of the noise added during perturbation using general-purpose data miners from the Weka software package, and an informed expert adversary is able to remove 91%–99.93% of the added noise. Interestingly, the higher the aimed privacy, the higher the percentage of noise can be removed. This suggests that adding more noise does not always increase the real privacy.

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Authors and Affiliations

  1. UNESCO Chair in Data Privacy, Department of Computer Engineering and Maths, Universitat Rovira i Virgili, Av. Paisos Catalans 26, 43007, Tarragona, Spain

    Michal Sramka

  2. Department of Applied Informatics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovicova 3, 81219, Bratislava, Slovakia

    Michal Sramka

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  1. Michal Sramka
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Correspondence to Michal Sramka .

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Editors and Affiliations

  1. , The Gateway, De Montfort University, Leicester, LE1 9BH, United Kingdom

    David A. Elizondo

  2. , Department of Computer Engineering and, Universitat Rovira i Virgili, Av. Paisos Catalans 26, Tarragona, 43007, Spain

    Agusti Solanas

  3. , Department of Computer Engineering and, Universitat Rovira i Virgili, Av. Paisos Catalans 26, Tarragona, 43007, Spain

    Antoni Martinez-Balleste

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Sramka, M. (2012). Breaching Privacy Using Data Mining: Removing Noise from Perturbed Data. In: Elizondo, D., Solanas, A., Martinez-Balleste, A. (eds) Computational Intelligence for Privacy and Security. Studies in Computational Intelligence, vol 394. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25237-2_9

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  • DOI: https://doi.org/10.1007/978-3-642-25237-2_9

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  • Print ISBN: 978-3-642-25236-5

  • Online ISBN: 978-3-642-25237-2

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