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A Survey of Privacy Preserving Utility Mining

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High-Utility Pattern Mining

Part of the book series: Studies in Big Data ((SBD,volume 51))

Abstract

High-utility pattern mining has emerged as an important research topic in data mining. It aims at discovering patterns having a high utility (e.g. profit or weight) in transaction or sequence databases. HUPM can be applied in various fields such as market basket analysis, website clickstream analysis, stock market analysis, retail and bioinformatics. In the era of information technology, it has become easy to locate and access information. A greater access to information has many benefits. However, it may also lead to privacy threats if datasets containing sensitive and important information are shared and made public. Therefore, privacy preservation has become a critical challenge for data mining. This chapter provides an up-to-date survey on privacy preserving utility mining (PPUM). The main purpose is to provide a general overview of recent techniques and algorithms for PPUM. The chapter focuses on research on both privacy preserving high-utility itemset mining and privacy preserving high-utility sequential pattern mining. Key concepts and terminology are introduced and discussed. Moreover, latest solutions for PPUM are compared. Finally, challenges and opportunities related to PPUM are discussed.

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Acknowledgements

This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 102.05-2018.307.

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Authors and Affiliations

  1. School of Knowledge Science, Japan Advanced Institute of Science and Technology, Ishikawa, Japan

    Duy-Tai Dinh & Van-Nam Huynh

  2. VNU-HCMC, Department of Computer Sciences, University of Sciences, Ho Chi Minh City, Vietnam

    Bac Le & Ut Huynh

  3. School of Humanities and Social Sciences, Harbin Institute of Technology (Shenzhen), Shenzhen, China

    Philippe Fournier-Viger

  4. Academy of Cryptography Techniques, Ho Chi Minh City, Vietnam

    Quang-Minh Nguyen

Authors
  1. Duy-Tai Dinh
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  2. Van-Nam Huynh
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  3. Bac Le
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  4. Philippe Fournier-Viger
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  5. Ut Huynh
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  6. Quang-Minh Nguyen
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Corresponding author

Correspondence to Duy-Tai Dinh .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology (Shenzhen), Shenzhen, China

    Philippe Fournier-Viger

  2. Western Norway University of Applied Sciences, Bergen, Norway

    Jerry Chun-Wei Lin

  3. Université du Québec à Montréal, Montreal, QC, Canada

    Roger Nkambou

  4. Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam

    Bay Vo

  5. National Chiao Tung University, Hsinchu, Taiwan

    Vincent S. Tseng

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Dinh, DT., Huynh, VN., Le, B., Fournier-Viger, P., Huynh, U., Nguyen, QM. (2019). A Survey of Privacy Preserving Utility Mining. In: Fournier-Viger, P., Lin, JW., Nkambou, R., Vo, B., Tseng, V. (eds) High-Utility Pattern Mining. Studies in Big Data, vol 51. Springer, Cham. https://doi.org/10.1007/978-3-030-04921-8_8

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