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HiMod-Pert: Histogram Modification Based Perturbation Approach for Privacy Preserving Data Mining

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Future Internet Technologies and Trends (ICFITT 2017)

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Abstract

Privacy Preserving Data Mining (PPDM) protects the disclosure of sensitive quasi-identifiers of dataset during mining by perturbing the data. This perturbed dataset is then used by trusted Third Party for effective derivation of association rules. Many PPDM algorithms destroy the original data to generate the mining results. It is essential that the perturbed data preserves the statistical inference of the sensitive attributes and minimize the information loss. Existing techniques based on Additive, Multiplicative and Geometric Transformations have minimal information loss, but suffer from reconstruction vulnerabilities. We propose Histogram Modification based method, viz. HiMod-Pert, for preserving the sensitive numeric attributes of perturbed dataset. Our method uses the difference in neighboring values to determine the perturbation factor. Experiments are performed to implement and test the applicability of the proposed technique. Evaluation using descriptive statistic metrics shows that the information loss is minimal.

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Authors and Affiliations

  1. MCA Department, Sarvajanik College of Engineering and Technology, Surat, India

    Alpa Kavin Shah

  2. Department of Computer Science, Veer Narmad South Gujarat University, Surat, India

    Ravi Gulati

Authors
  1. Alpa Kavin Shah
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  2. Ravi Gulati
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Correspondence to Alpa Kavin Shah .

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Editors and Affiliations

  1. Sardar Vallabhbhai National Institute of Technology, Surat, India

    Zuber Patel

  2. Sardar Vallabhbhai National Institute of Technology, Surat, India

    Shilpi Gupta

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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Shah, A.K., Gulati, R. (2018). HiMod-Pert: Histogram Modification Based Perturbation Approach for Privacy Preserving Data Mining. In: Patel, Z., Gupta, S. (eds) Future Internet Technologies and Trends. ICFITT 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 220. Springer, Cham. https://doi.org/10.1007/978-3-319-73712-6_3

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  • DOI: https://doi.org/10.1007/978-3-319-73712-6_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73711-9

  • Online ISBN: 978-3-319-73712-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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