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Workshop on Mining Data for Financial Applications

International Workshop on Personal Analytics and Privacy

MIDAS 2018, PAP 2018: ECML PKDD 2018 Workshops pp 141–155Cite as

Privacy Risk for Individual Basket Patterns

Part of the Lecture Notes in Computer Science book series (LNAI,volume 11054)

Abstract

Retail data are of fundamental importance for businesses and enterprises that want to understand the purchasing behaviour of their customers. Such data is also useful to develop analytical services and for marketing purposes, often based on individual purchasing patterns. However, retail data and extracted models may also provide very sensitive information to possible malicious third parties. Therefore, in this paper we propose a methodology for empirically assessing privacy risk in the releasing of individual purchasing data. The experiments on real-world retail data show that although individual patterns describe a summary of the customer activity, they may be successful used for the customer re-identifiation.

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Acknowledgments

Work partially supported by the EU H2020 Program under the funding scheme “INFRAIA-1-2014-2015: Research Infrastructures”, grant agreement 654024 “SoBigData” (http://www.sobigdata.eu).

Author information

Authors and Affiliations

  1. University of Pisa, Largo B. Pontecorvo, 3, Pisa, Italy

    Roberto Pellungrini, Anna Monreale & Riccardo Guidotti

  2. KDDLab, ISTI-CNR, Via G. Moruzzi, 1, Pisa, Italy

    Riccardo Guidotti

Authors
  1. Roberto Pellungrini
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  2. Anna Monreale
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  3. Riccardo Guidotti
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Corresponding author

Correspondence to Roberto Pellungrini .

Editor information

Editors and Affiliations

  1. IBM Research - Ireland, Dublin, Ireland

    Carlos Alzate

  2. KDD Lab, University of Pisa, Pisa, Italy

    Anna Monreale

  3. University of Turin, Turin, Italy

    Livio Bioglio

  4. R&D Department, UniCredit, Milan, Italy

    Valerio Bitetta

  5. R&D Department, UniCredit, Rome, Italy

    Dr. Ilaria Bordino

  6. IMT School for Advanced Studies Lucca, Lucca, Italy

    Guido Caldarelli

  7. R&D Department, UniCredit, Milan, Italy

    Andrea Ferretti

  8. KDD Lab, University of Pisa, Pisa, Italy

    Riccardo Guidotti

  9. R&D Department, UniCredit, Rome, Italy

    Francesco Gullo

  10. R&D Department, UniCredit, Milan, Italy

    Stefano Pascolutti

  11. Department of Computer Science, University of Turin, Turin, Italy

    Prof. Dr. Ruggero G. Pensa

  12. National Institute of Applied Science, Université de Lyon, Lyon, France

    Celine Robardet

  13. IMT School Advanced Studies Lucca, Lucca, Italy

    Dr. Tiziano Squartini

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Pellungrini, R., Monreale, A., Guidotti, R. (2019). Privacy Risk for Individual Basket Patterns. In: , et al. ECML PKDD 2018 Workshops. MIDAS PAP 2018 2018. Lecture Notes in Computer Science(), vol 11054. Springer, Cham. https://doi.org/10.1007/978-3-030-13463-1_11

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  • DOI: https://doi.org/10.1007/978-3-030-13463-1_11

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