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Frequent Sets Discovery in Privacy Preserving Quantitative Association Rules Mining

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Hybrid Artificial Intelligent Systems (HAIS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9121))

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Abstract

This paper deals with discovering frequent sets for quantitative association rules mining with preserved privacy. It focuses on privacy preserving on an individual level, when true individual values, e.g., values of attributes describing customers, are not revealed. Only distorted values and parameters of the distortion procedure are public. However, a miner can discover hidden knowledge, e.g., association rules, from the distorted data. In order to find frequent sets for quantitative association rules mining with preserved privacy, not only does a miner need to discretise continuous attributes, transform them into binary attributes, but also, after both discretisation and binarisation, the calculation of the distortion parameters for new attributes is necessary. Then a miner can apply either MASK (Mining Associations with Secrecy Konstraints) or MMASK (Modified MASK) to find candidates for frequent sets and estimate their supports. In this paper the methodology for calculating distortion parameters of newly created attributes after both discretisation and binarisation of attributes for quantitative association rules mining has been proposed. The new application of MMASK for finding frequent sets in discovering quantitative association rules with preserved privacy has been also presented. The application of MMASK scheme for frequent sets mining in quantitative association rules discovery on real data sets has been experimentally verified. The results of the experiments show that both MASK and MMASK can be applied in frequent sets mining for quantitative association rules with preserved privacy, however, MMASK gives better results in this task.

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Notes

  1. 1.

    The authors use Konstraints instead of Constraints to achieve abbreviation: MASK.

  2. 2.

    In real applications a true data set is not stored. Only distorted tuples are collected.

  3. 3.

    For details about the probabilities of changing/retaining the original value and the matrix of retaining/changing values of nominal attribute please refer to [12].

  4. 4.

    In this paper, the term item and binary attribute is used interchangeably.

  5. 5.

    \(\mathbf {P}'\) is a matrix of retaining/changing values of discretised (nominal) attribute.

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Authors and Affiliations

  1. Institute of Computer Science, Warsaw University of Technology, Warsaw, Poland

    Piotr Andruszkiewicz

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  1. Piotr Andruszkiewicz
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Correspondence to Piotr Andruszkiewicz .

Editor information

Editors and Affiliations

  1. University of Deusto, Bilbao, Spain

    Enrique Onieva

  2. University of Deusto, Bilbao, Spain

    Igor Santos

  3. University of Deusto, Bilbao, Spain

    Eneko Osaba

  4. Universidad de Salamanca, Salamanca, Spain

    Héctor Quintián

  5. Universidad de Salamanca, Salamanca, Spain

    Emilio Corchado

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Andruszkiewicz, P. (2015). Frequent Sets Discovery in Privacy Preserving Quantitative Association Rules Mining. In: Onieva, E., Santos, I., Osaba, E., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2015. Lecture Notes in Computer Science(), vol 9121. Springer, Cham. https://doi.org/10.1007/978-3-319-19644-2_1

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  • DOI: https://doi.org/10.1007/978-3-319-19644-2_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19643-5

  • Online ISBN: 978-3-319-19644-2

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