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Generalization-Based k-Anonymization

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Modeling Decisions for Artificial Intelligence (MDAI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9321))

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Abstract

Microaggregation is an anonymization technique consisting on partitioning the data into clusters no smaller than k elements and then replacing the whole cluster by its prototypical representant. Most of microaggregation techniques work on numerical attributes. However, many data sets are described by heterogeneous types of data, i.e., numerical and categorical attributes. In this paper we propose a new microaggregation method for achieving a compliant k-anonymous masked file for categorical microdata based on generalization. The goal is to build a generalized description satisfied by at least k domain objects and to replace these domain objects by the description. The way to construct that generalization is similar that the one used in growing decision trees. Records that cannot be generalized satisfactorily are discarded, therefore some information is lost. In the experiments we performed we prove that the new approach gives good results.

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Acknowledgments

This research is partially funded by the Spanish MICINN projects COGNITIO (TIN-2012-38450-C03-03), EdeTRI (TIN2012-39348-C02-01) and COPRIVACY (TIN2011-27076-C03-03), the grant 2009-SGR-1434 from the Generalitat de Catalunya, and the European Project DwB (Grant Agreement Number 262608).

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Authors and Affiliations

  1. CSIC - Spanish Council for Scientific Research, IIIA - Artificial Intelligence Research Institute, Campus UAB, 08193, Bellaterra, Catalonia, Spain

    Eva Armengol

  2. University of Skövde, Skövde, Sweden

    Vicenç Torra

Authors
  1. Eva Armengol
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  2. Vicenç Torra
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Corresponding author

Correspondence to Vicenç Torra .

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Editors and Affiliations

  1. University of Skövde, Skövde, Sweden

    Vicenc Torra

  2. Toho Gakuen, Tokyo, Japan

    Torra Narukawa

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Armengol, E., Torra, V. (2015). Generalization-Based k-Anonymization. In: Torra, V., Narukawa, T. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2015. Lecture Notes in Computer Science(), vol 9321. Springer, Cham. https://doi.org/10.1007/978-3-319-23240-9_17

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  • DOI: https://doi.org/10.1007/978-3-319-23240-9_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23239-3

  • Online ISBN: 978-3-319-23240-9

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