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Privacy Preserving Data Mining for Deliberative Consultations

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Book cover Hybrid Artificial Intelligent Systems (HAIS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9648))

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Abstract

In deliberative consultations, which utilise electronic surveys as a tool to obtain information from residents, preserving privacy plays an important role. In this paper investigation of a possibility of privacy preserving data mining techniques application in deliberative consultations has been conducted. Three main privacy preserving techniques; namely, heuristic-based, reconstruction-based, and cryptography-based have been analysed and a setup for online surveys performed within deliberative consultations has been proposed.

This work can be useful for designers and administrators in the assessment of the privacy risks they face with a system for deliberative consultations. It can also be used in the process of privacy preserving incorporation in such a system in order to help minimise privacy risks to users.

This work has been supported by the National Centre for Research and Development under Grant No. IS-1/072/NCBR/2014 and the Institute of Computer Science, Warsaw University of Technology under Grant No. II/2015/DS/1.

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Notes

  1. 1.

    The proof in the case of privacy preserving association rules mining can be found in [16].

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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 .

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Editors and Affiliations

  1. Universidad Pablo de Olavide, Sevilla, Spain

    Francisco Martínez-Álvarez

  2. Universidad Pablo de Olavide, Sevilla, Spain

    Alicia Troncoso

  3. University of Salamanca, Salamanca, Spain

    Héctor Quintián

  4. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Andruszkiewicz, P. (2016). Privacy Preserving Data Mining for Deliberative Consultations. In: Martínez-Álvarez, F., Troncoso, A., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2016. Lecture Notes in Computer Science(), vol 9648. Springer, Cham. https://doi.org/10.1007/978-3-319-32034-2_9

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  • DOI: https://doi.org/10.1007/978-3-319-32034-2_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32033-5

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