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A Sanitization Approach of Privacy Preserving Utility Mining

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Genetic and Evolutionary Computing (GEC 2015)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 388))

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  • International Conference on Genetic and Evolutionary Computing

Abstract

High-Utility Itemset Mining (HUIM) considers both quantity and profit factors to measure whether an item or itemset is a profitable product. With the rapid growth of security considerations, privacy-preserving utility mining (PPUM) has become a critical issue in HUIM. In this paper, an efficient algorithm is proposed to minimize side effects in the sanitization process for hiding sensitive high utility itemsets. Three similarity measurements are also designed as the new standard used in PPUM. Experiments are also conducted to show the performance of the designed algorithm in terms of general side effects in PPDM and the new defined measurements in PPUM.

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Author information

Authors and Affiliations

  1. Innovative Information Industry Research Center(IIIRC), School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China

    Jerry Chun-Wei Lin, Tsu-Yang Wu & Guo Lin

  2. Department of Computer Science, University of Moncton, Moncton, Canada

    Philippe Fournier-Viger

  3. Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, 811, Taiwan, R.O.C

    Tzung-Pei Hong

  4. Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, 804, Taiwan, R.O.C.

    Tzung-Pei Hong

  5. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, 350118, China

    Jeng-Shyang Pan

Authors
  1. Jerry Chun-Wei Lin
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  2. Tsu-Yang Wu
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  3. Philippe Fournier-Viger
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  4. Guo Lin
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  5. Tzung-Pei Hong
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  6. Jeng-Shyang Pan
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Corresponding author

Correspondence to Jerry Chun-Wei Lin .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Miyazaki, Miyazaki, Japan

    Thi Thi Zin

  2. School of Computer Science and Tech..., Harbin Institute of Technology, Shenzhen Graduate School , Shenzhen, China

    Jerry Chun-Wei Lin

  3. College of Information Science and Engg, Fujian University of Technology, Fuzhou, China

    Jeng-Shyang Pan

  4. Faculty of Engineering, University of Miyazaki, Miyazaki, Japan

    Pyke Tin

  5. Faculty of Engineering, University of Miyazaki, Miyazaki, Japan

    Mitsuhiro Yokota

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© 2016 Springer International Publishing Switzerland

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Lin, J.CW., Wu, TY., Fournier-Viger, P., Lin, G., Hong, TP., Pan, JS. (2016). A Sanitization Approach of Privacy Preserving Utility Mining. In: Zin, T., Lin, JW., Pan, JS., Tin, P., Yokota, M. (eds) Genetic and Evolutionary Computing. GEC 2015. Advances in Intelligent Systems and Computing, vol 388. Springer, Cham. https://doi.org/10.1007/978-3-319-23207-2_6

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  • DOI: https://doi.org/10.1007/978-3-319-23207-2_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23206-5

  • Online ISBN: 978-3-319-23207-2

  • eBook Packages: EngineeringEngineering (R0)

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