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A Heuristic Approach for Sensitive Pattern Hiding with Improved Data Quality

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New Frontiers in Mining Complex Patterns (NFMCP 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11948))

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Abstract

Frequent itemset mining can be used to discover various interesting patterns present in dataset. However, this imposes a great privacy threat when data is shared with other organisations. There are some business critical frequent patterns that are considered as sensitive from organization’s or individual’s perspective because revealing such patterns can disclose confidential information. Privacy preserving data mining (PPDM) provides various techniques to hide sensitive patterns to make sure that they cannot be revealed by applying data mining models on shared datasets. Heuristic based sensitive pattern hiding techniques are widely adopted PPDM techniques due to their fast execution time but causes high side effects. In this paper, we propose a heuristic approach for sensitive pattern hiding based on deletion of Victim items which is named MinMax. In the proposed algorithm, Misses Cost Impact (MCI) value of each tentative Victim item is calculated and item with minimum MCI is selected as Victim item resulting in low Misses Cost. Experimental results on benchmark datasets show that proposed algorithm achieves better data quality with less execution time as compared to existing heuristic based techniques.

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Authors and Affiliations

  1. Indian Institute of Technology Roorkee, Roorkee, 247667, India

    Shalini Jangra & Durga Toshniowal

Authors
  1. Shalini Jangra
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  2. Durga Toshniowal
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Corresponding author

Correspondence to Shalini Jangra .

Editor information

Editors and Affiliations

  1. University of Bari Aldo Moro, Bari, Italy

    Michelangelo Ceci

  2. University of Bari Aldo Moro, Bari, Italy

    Corrado Loglisci

  3. CNR-ICAR, Rende, Italy

    Giuseppe Manco

  4. Federico II University, Naples, Italy

    Elio Masciari

  5. University of North Carolina, Charlotte, NC, USA

    Zbigniew Ras

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Jangra, S., Toshniowal, D. (2020). A Heuristic Approach for Sensitive Pattern Hiding with Improved Data Quality. In: Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2019. Lecture Notes in Computer Science(), vol 11948. Springer, Cham. https://doi.org/10.1007/978-3-030-48861-1_2

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  • DOI: https://doi.org/10.1007/978-3-030-48861-1_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-48860-4

  • Online ISBN: 978-3-030-48861-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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